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To improve the cell productivity of Corynebacterium glutamicum , its initial specific growth rate was improved by medium improvement using deep neural network (DNN)-assisted design with Bayesian optimization (BO) and a genetic algorithm (GA). To obtain training data for the DNN, experimental design with an orthogonal array was set up using a chemically defined basal medium (GC XII). Based on the cultivation results for the training data, specific growth rates were observed between 0.04 and 0.3/h. The resulting DNN model estimated the test data with high accuracy (R2 test ≥ 0.98). According to the validation cultivation, specific growth rates in the optimal media components estimated by DNN-BO and DNN-GA increased from 0.242 to 0.355/h. Using the optimal media (UCB_3), the specific growth rate, along with other parameters, was evaluated in batch culture. The specific growth rate reached 0.371/h from 3 to 12 h, and the dry cell weight was 28.0 g/L at 22.5 h. From the cultivation, the cell yields against glucose, ammonium ion, phosphate ion, sulfate ion, potassium ion, and magnesium ion were calculated. The cell yield calculation was used to estimate the required amounts of each component, and magnesium was found to limit the cell growth. However, in the follow-up fed-batch cultivation, glucose and magnesium addition was required to achieve the high initial specific growth rate, while appropriate feeding of glucose and magnesium during cultivation resulted in maintaining the high specific growth rate, and obtaining a cell yield of 80 g/L ini. • A novel growth medium for Corynebacterium glutamicum was developed using deep neural networks and optimization algorithms. • During the validation cultivation, high specific growth rates were observed in the proposed media. • The limiting factors for high biomass yield were identified in the proposed medium. • To optimize productivity and yield, a fed-batch culture was designed using the proposed medium. [ABSTRACT FROM AUTHOR]

To solve the position control problem of the induction motor with parameter perturbations, load disturbances, and modeling errors, a predefined-time position tracking optimization control method with prescribed performance is proposed. In practice, the rotor flux linkage of the induction motor can't be measured, and a predefined-time sliding mode observer (PTSMO) is applied to accurately estimate it. Additionally, predefined-time disturbance observers (PTDOs) are employed to identify the uncertainties in the motor system. The position and flux linkage controllers are then designed by integrating the predefined-time control approach with the prescribed performance function method, realizing accurate tracking control of the induction motor within a predefined time. Next, the adaptive genetic algorithm (AGA) and the improved particle swarm optimization (IPSO) technique are combined to optimize the designed controllers, enhancing the convergence rate and steady-state accuracy of the induction motor. Finally, comparative analyses through simulations and dSPACE simulated experiments validate the efficacy of the proposed control method, highlighting its applicability in practical motor systems. ● The predefined-time sliding mode observer is crafted to observe the rotor flux linkage, and the predefined-time disturbance observers are designed to estimate the system's uncertainties. This not only addresses the challenges of unmeasurable rotor flux linkage in practice, and the observation errors converge within a predefined time. ● This paper combines the predefined-time control and the prescribed performance method, ensuring both the transient and steady-state performances of the system, while also ensuring the motor states converge within a predefined time. ● This paper combines the AGA and the IPSO to generate the AGA_IPSO hybrid algorithm to optimize the primary controller parameters, which not only simplifies the tuning process of system parameters, but also enhances the system's convergence speed and steady-state accuracy.. [ABSTRACT FROM AUTHOR]

Regasification of liquefied natural gas (LNG) releases significant cooling potential, but improper usage can easily lead to wastage of resources and environmental pollution. The study developed an integrated air separation system and Organic Rankine Cycle (ORC) power generation system to effectively harness the inherent cold energy within LNG. Two distinct optimization algorithms, the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and the Particle Swarm Optimization (PSO), were employed to determine the optimal operational parameters. The results showed the superior performance of PSO over NSGA-II, as demonstrated by the exergy efficiency, net output power, and levelized cost of energy at 78.12 %, 2.29*1011 kW, and 0.0665 $/kWh, respectively. Additionally, an assessment of various factors influencing the system's performance was conducted. The performance of both conventional and proposed systems was studied under varying regasification rates. The findings indicated that incorporating the cold recovery and storage unit enhanced the efficient utilization of LNG cold energy, resulting in an increased valley-to-peak ratio of the total power output from 0.03 in the conventional system to 0.5. The proposed system exhibits alignment with the energy consumption demands of users, and this study is anticipated to furnish a theoretical foundation for harnessing the cooling capacity of liquid hydrogen. [Display omitted] • The novel utilization of LNG cold energy by combining air separation with ORC. • PSO is superior than NSGA-II for optimization: 14.4 % decrement in LCOE. • PSO: 17.9 % and 7.0 % increment in the exergy efficiency and net output power. • Both LNG steady state and fluctuating in regasification rates were investigated. • The valley-to-peak ratio of the system's power output was increased to 0.5. [ABSTRACT FROM AUTHOR]

A two-step optimization algorithm is proposed to redesign a precision reference repeating sun-synchronous orbit for interferometric synthetic aperture radar satellites with injection error. This purpose is to design a new reference orbit for the satellite that deviates from the original preset orbit. Manoeuvring satellites to this new orbit consumes less propellant than manoeuvring to the original orbit. In addition, the repeatability of the orbit is a guarantee for obtaining good interference images, which puts higher requirements on the revisit accuracy of the redesign reference orbit. In the first step, the initial guess for reference orbital elements is designed by considering the global coverage of the Earth and the propellant consumption, taking into account the J 2 and J 4 perturbations. A search table of feasible solutions in the neighbourhood of the injection orbit is established. This approach, which is based on a search table, transforms the optimization of continuous variables in infinite space into the traversal of discrete variables in finite space, thus greatly reducing the optimization time. In the second step, the revisit accuracy of the satellite's full-dimensional state vector is optimized under the orbit propagation of a high-order gravity field. Unlike traditional methods that only optimize the revisit error of position, this paper also optimizes that of velocity. A new optimization method for full-dimensional revisit error of state vector is proposed, which combines nested differential correction with non-dominated sorting genetic algorithm-II. Compared with the traditional genetic algorithms, the proposed nested differential correction method provides the non-dominated sorting genetic algorithm-II with more accurate initial values, which reduces the iterations. Finally, the simulation results show that the total velocity change from the actual injection orbit to the redesigned reference orbit is halved compared to the original preset orbit. Moreover, the revisit errors of position and velocity are within 1 m and 1 m / s , respectively. [ABSTRACT FROM AUTHOR]

The 0–1 Knapsack problem is a non-deterministic polynomial-time-hard combinatorial optimization problem that cannot be solved in reasonable time using traditional methods. Therefore, researchers have turned to metaheuristic algorithms for their ability to solve several combinatorial problems in a reasonable amount of time. This paper adapts the Kepler optimization algorithm using eight V-shaped and S-shaped transfer functions to create a binary variant called BKOA for solving the 0–1 Knapsack problem. Several experiments were conducted to compare the efficacy of the binary Kepler optimization algorithm to several competing optimizers when solving 20 well-known knapsack instances with dimensions ranging from 4 to 75. The experimental results demonstrate the superiority of this algorithm over other metaheuristic algorithms, except for the genetic algorithm, which is marginally superior. To further improve the binary Kepler optimization algorithm, it is combined with an enhanced improvement strategy to create a new hybrid variant. This hybrid variant, termed HBKOA, has superior exploration and exploitation capabilities that make it better than genetic algorithm and other optimizers for all performance metrics considered. The enhanced improvement strategy is also integrated with several competing optimizers, and the experimental results show that HBKOA, hybrid binary manta ray foraging optimization, and hybrid binary equilibrium optimizer are competitive for small and medium-dimensional instances and superior for the higher dimensions. [ABSTRACT FROM AUTHOR]

• Two-level optimization solves the maneuver planning for multiple spacecraft close-range rendezvous. • Multi-impulse solved by trajectory planning is used in each rendezvous. • An impulsive correction method is proposed for precise rendezvous. • The surrogate model built by FNN accurately predicts the impulsive velocity increase for rendezvous. Rendezvous mission planning between service spacecraft and multiple close-range target spacecraft in the on-orbit service application is studied, using multiple impulses based on the Clohessy-Wiltshire equation as a maneuvering strategy. An impulsive correction method is proposed to avoid rendezvous errors. The optimization objectives of this work include the minimum total velocity increase and the minimum mission time. A two-level optimization approach is developed to find the Pareto-optimal set of the above multi-objective optimization problem. In the low-level optimization, the propellant-optimal impulsive trajectory for the rendezvous between the SSc and each target spacecraft at a fixed maneuver duration is solved by a proposed iterative optimization algorithm with a double loop. On the other hand, the rendezvous sequence and maneuver duration are optimized by a proposed neural-network-assisted evolutionary algorithm in up-level optimization, which combines the feedforward neural network with the elitist multi-objective genetic algorithm (NSGA-II). Finally, representative simulation examples are presented to verify the proposed optimization algorithm and demonstrate its superiority over the traditional algorithm. [ABSTRACT FROM AUTHOR]

Sizing optimization and power smoothing methodology in highly fluctuating grid-connected renewable systems represent important challenges nowadays, the total cost and energy quality depend on the effective balance between source and load with certain restrictions to increase reliability and enhance the use of renewable sources at all scales. This article presents a novel power smoothing effect and sizing optimization methodology for a grid-connected photovoltaic-hydrokinetic system by pattern search method comparatively with three optimization algorithms encompassing a multi-objective optimization. In this context, the method considers the reduction of power fluctuations using supercapacitors, where the objective function considers technical and economic indexes. The main contribution of the research is that the objective function decreases the fluctuation suppression ratio to sizing optimization of renewable system through three pattern search size optimization algorithms. Experiments have shown that pattern search method can find the global optimum by reducing the computational effort; the Latin hypercube algorithm reduces the photovoltaic capacity by approximately 5 % and 2.3 % with respect to the Nelder Mead and genetic algorithm respectively. The influence of the supercapacitor successfully reduces power fluctuations and causes an increase in self-consumption of 32.32 %, this result represents an annual cost savings of 42 % on energy purchased from the grid. Furthermore, this paper goes further by calculating the optimal location of a hydrokinetic turbine in a river using the HEC-RAS software to determine with high precision the HKT power fluctuations as input signal to novel proposed method. [Display omitted] • Hybrid supercapacitor-integrated dispatch model with analysis of pattern search method. • Dispatch-coupled sizing renewable sources and energy storage with power smoothing, improving energy quality. • Numerical analysis on different size optimization methods comparatively considering experimental data. • An improved pattern search optimization that exceeds the standard one. • Validation of the effectiveness of the proposed framework in a laboratory microgrid test system. [ABSTRACT FROM AUTHOR]

A photovoltaic-thermoelectric (PV-TE) system is modeled theoretically and demonstrated experimentally. Mathematical formulations of the system are proposed according to energy conservation and finite-time thermodynamics, which can be applicable to analyze and optimize the system that consists of commercial photovoltaic (PV) cells and thermoelectric generators (TEGs). The genetic algorithm is introduced to maximize the system's power by optimizing multiple parameters. In Yan'an City, which is located in the northern Shaanxi Province in northwest China, the actual solar irradiance is recorded for almost a full year and the proposed model is verified. Theoretical and experimental results are analyzed and compared. It is found that there are several independent structure parameters in the PV-TE systems and they should be optimized simultaneously to maximize the performance. The PV-TE system's maximum theoretical average power of 0.343 W is 2.69 % greater than the solo PV cell's maximum theoretical power of 0.334 W. The measured average power of the system of 0.317 W is 0.32 % more than that of a solo PV cell of 0.316 W. The duration of strong lighting in Yan'an City reaches 8 h every day for nearly one year and the irradiation intensity can approach 1000 W/m2. The solar radiance is weakly affected by the seasonal changes of four seasons but is greatly affected by rainy days and cloud thickness. The research findings can serve as a helpful guide for the specific implementation of the PV-TE system and exploitation of solar energy resources in northwest China. • The novel model of a tandem commercial PV-TE system is established. • The genetic algorithm optimizes parameters to maximize the power of the system. • The maximum power of the system is 2.69 % higher than that of a solo PV cell. • The experiment power of the system is 0.32 % higher than that of a solo PV cell. [ABSTRACT FROM AUTHOR]

• Study of PV/Biomass/Battery system for a remote village in Bangladesh. • Probabilistic modeling to address the uncertainties of resources and load demand. • Optimization with three different algorithms for effective global minima finding. • Performance superiority of Dandelion Optimizer (DO). • Obtained results provide technical and economic margins for stakeholders. Hybrid Renewable Energy System (HRES) has become a popular alternative for locations restricted to national grid connection due to geographical limitations. The study investigates the available renewable resources of a remote village in Mymensingh district of Bangladesh to propose and evaluate optimal sizing and cost of a grid independent HRES. The intermittency of solar irradiance and uncertain variation in load demand are taken into account by adopting probabilistic scenario-based analysis (SBA). Beta distribution and Gaussian distribution are considered for solar irradiance and load uncertainties, respectively, and Roulette Wheel mechanism generates multiple probabilistic scenarios. Objective function is formulated to minimize the total system cost (TSC) under defined constraints. Dandelion optimizer (DO), a relatively recent and unexplored metaheuristic algorithm along with two other popular optimization algorithms, Slime Mould Algorithm (SMA) and Real Coded Genetic Algorithm (RCGA) are applied to size the components for different probabilistic scenarios generated by Roulette Wheel. DO outperformed SMA and RCGA providing the solution set with 18.4 % and 3.2 % reduction in simulation time and system cost, respectively, in comparison with RCGA, whereas, 11.3 % and 16.7 % reduction in simulation time and system cost, respectively, in contrast to SMA. DO's computation of cost margin (1.17 million dollars to 1.46 million dollars), PV allocation (minimum 7 modules to maximum 218 modules), biomass power allocation (maximum of 70.34215 kW) and optimized battery allocation computed by the algorithms (24 units) provide authorities a margin of cost-efficient estimation with conservative preparedness for developing HRES in the selected region. The study also provides a scientific framework to account for the uncertain parameters while proposing and analyzing HRES for off-grid localities, where inadequate literary exploration is evident in the context of Bangladesh. [ABSTRACT FROM AUTHOR]

• Various optimization design methods for high-power nanocrystalline alloy high-frequency transformer are analyzed and compared. • MOPSO has more advantages in the optimization design of high-frequency transformer. • The influence of temperature rise calculation methods on the optimization algorithm is analyzed. • The essence of improving the performance of high-frequency transformer through optimization methods is discussed. The optimization design of high-frequency transformer (HFT) is a multi-objective optimization problem owing to the mutual constraints between design parameters such as power density, efficiency and temperature rise, and traditional design methods often face difficulties in balancing and selecting multiple design parameters. Therefore, this paper derives the structural parameters of HFT, calculates the magnetic core losses and winding losses considering temperature effects, and establishes a 7-node thermal network model. Selecting power density and efficiency as optimization objectives, the HFT is optimized based on multi-objective particle swarm optimization algorithm and non-dominated genetic algorithm, and compared with traditional free parameter scanning method to analyze the advantages of intelligent optimization algorithm. Further, the influence of temperature rise calculation methods on the optimization design results of HFT is compared and analyzed. Finally, a nanocrystalline HFT with 17 MW/m3 power density and 99 % efficiency and an optimization scheme of HFT with 26 MW/m3 power density and 99.2 % efficiency are designed based on the proposed optimization design processes. The effectiveness of the calculation model and optimization method is verified through experimental measurement, and the essential principle of improving the HFT performance through optimization methods is explored through finite element simulation, providing theoretical reference and data support for the optimization design of nanocrystalline HFT. [ABSTRACT FROM AUTHOR]

Ultra-high Voltage Direct Current system (UHVDC) can realize long-distance transmission, and its carbon loss reduction is a focus in the power grid field.A multi-objective optimization model for low-carbon, economic, and safe operation on UHVDC is established, where we proposed a Residual Neural Network-Long Short-Term Memory (ResNet-LSTM) deep learning network as the carbon loss implicit objective function. The proposed ResNet-LSTM exhibits superior performance in carbon loss measurement for UHVDC, the experiments show that the Mean Squared Error (MSE) of ResNet-LSTM reaches 0.41%, which surpasses that of ResNet, LSTM, and Convolutional Neural Network- Long Short-Term Memory (CNN-LSTM) by 39.71%, 30.51%, and 21.15% respectively, while also increasing the R-Squared (R2) score by 1.48% on average. An Improved Non-dominated Sorting Genetic Algorithm II (Im-NSGA-II) algorithm is proposed to solve the model based on population initialization and crowding calculation improvements. The experiment results show that the two improvement strategies effectively reduce carbon losses and carbon trading costs. The proposed Im-NSGA-II outperforms the mainstream algorithm Adaptive Grid Evolutionary Multi-Objective Algorithm Based on Decomposition (AGE-MOEA/D), Reference Vector Guided Evolutionary Algorithm (RVEA), Sequentially Multi-Objective Selection based on the Efficient Multi-objective Optimization Algorithm (SMS-EMOA), and Non-dominated Sorting Genetic Algorithm II (NSGA-II), and decreases the other four algorithms on carbon losses and carbon trading costs by 34.73% and 14.74% on average, in the meanwhile, the transformer ratio errors are within the safe range. In the equipment of UHVDC, Direct Current (DC) transmission lines and converter transformers account for the largest proportion, which is the most worthy of attention in the future loss reduction work. After the above treatment, the UHVDC system can better achieve low-carbon economy and safe operation. [ABSTRACT FROM AUTHOR]

• Porous microstructure dictates the overall performance of a reactor. • Ractor microstructure is optimized by pore network modeling and metaheuristic optimization. • A multi-objective optimization led to a higher conversion rate and a lower hydraulic requirement. • The idea of background grid is presented for a versatile topological optimization of the pore network. Reactive transport within porous reactors is crucial to many diverse applications, and the efficacy of these reactors hinges on their microstructure. Mathematical modeling and optimization play a pivotal role in the exploration of efficient designs, enabling the generation of structures that may not be achievable through random realizations of packings. In this study, we propose a framework for high-resolution topological optimization of porous flow-through reactors based on pore-scale simulations using a non-dominated sorting genetic algorithm II. A pore network model for an advection–diffusion-reaction system is developed to simulate reactor performance. This model is integrated with a mathematical optimization algorithm, incorporating a background grid and Delaunay tessellation. The optimization framework generates enhanced porous structures, simultaneously maximizing conversion rates while minimizing pumping costs. Striking a balance between permeability and reactive surface area, the final designs yield a set of Pareto optimal solutions, encompassing diverse non-dominated designs with varying reaction rates and hydraulic requirements. The results demonstrate that optimal pore configurations lead to a 280% increase in conversion rates and a 6% reduction in pumping costs at one end, while on the opposite end of the Pareto front, a 15.2% increase in reaction rates and an 11.3% reduction in pumping costs are observed. [ABSTRACT FROM AUTHOR]

Wireless Sensor Network (WSN) is a set of several sensor nodes that are used for monitoring heterogeneous physical objects. In WSNs, irregular and bursty traffic Leads to the congestion problem, which incites a decrease in Packet Delivery Ratio (PDR) and increases packet loss as well as end-to-end delay. In the recent era, manifold efforts have been carried out to reduce network congestion however, these solutions have slow and premature optimization. To address optimization issues, this paper presents a self-adaptive source-sending rate optimization algorithm, which is a hybrid version of Non-dominated Sorting Genetic Algorithm III (NSGA-III) and Bifold-objective Proportional Integral Derivative (BPID) called N3-BPID. These techniques play a significant role in optimizing source rates to reduce network congestion. NSGA-III is a reference-based evolutionary approach, which dynamically configures the PID coefficients to get an optimal response. Furthermore, a novel bifold-objective fitness function is designed that balances the trade-offs between two PIDs performance indexes such as the Integral of Absolute Error and the Integral of Square Error. Due to simplicity and efficiency, an identically weighted aggregation mechanism is applied to ensemble both objectives into a single one. The proposed work is implemented to demonstrate a smart border surveillance application using Network Simulator v3 and compared with the state-of-the-art congestion control model Cuckoo Fuzzy PID (CFPID). The experimental result reveals that the proposed algorithm has significantly outperformed existing schemes in terms of PDR by 6.82%, packet loss by 24.52%, end-to-end delay by 15.31%, and queue length deviation by 8.93%. [ABSTRACT FROM AUTHOR]

• The first comprehensive review of optimization algorithms on microchannels is presented. • Status of topology optimization algorithms in microchannels energy transfer are summarized. • The theoretical basis and practical process of genetic algorithms in microchannels are carried out. • The characteristics of different algorithms are given and prospective direction is put forward. With the increasing demand for microchannel thermal management technology, a large number of researchers devote their energies to the optimization of microchannel. However, the optimization design based on labor and experience is not only inefficient but also lacks sufficient systematic quantitative analysis. In this paper, the relevant achievements in recent decades are investigated, finding that more and more scientists are turning their attention to the optimization algorithm field to solve the optimization problem of microchannel accurately. At present, the two most widely used optimization algorithms are topology optimization algorithm (TOA) and genetic algorithm (GA). Firstly, the author summarizes the development of the two optimization algorithms in the fields of microchannel and microchannel heat sink, and further studies the application status of the improved optimization algorithm in the design of microchannel heat sink (MCHS). Finally, the advantages and disadvantages of different algorithms and their development prospects are summarized. [ABSTRACT FROM AUTHOR]

At any moment in time, evolution is faced with a formidable challenge: refining the already highly optimised design of biological species, a feat accomplished through all preceding generations. In such a scenario, the impact of random changes (the method employed by evolution) is much more likely to be harmful than advantageous, potentially lowering the reproductive fitness of the affected individuals. Our hypothesis is that ageing is, at least in part, caused by the cumulative effect of all the experiments carried out by evolution to improve a species' design. These experiments are almost always unsuccessful, as expected given their pseudorandom nature, cause harm to the body and ultimately lead to death. This hypothesis is consistent with the concept of "terminal addition", by which nature is biased towards adding innovations at the end of development. From the perspective of evolution as an optimisation algorithm, ageing is advantageous as it allows to test innovations during a phase when their impact on fitness is present but less pronounced. Our inference suggests that ageing has a key biological role, as it contributes to the system's evolvability by exerting a regularisation effect on the fitness landscape of evolution. [ABSTRACT FROM AUTHOR]

To promote the continuous operating of solar energy systems and increase the proportion of renewable energy consumption, solar devices are often integrated with conventional energy systems to reduce greenhouse gas emissions. In this study, a solar-natural gas coupling system is constructed, utilizing solar photovoltaics, thermal collectors, and high-efficiency energy conversion devices. After modelling the energy system, the operating modes are illustrated considering working sequences of water-cooled devices. A modified genetic algorithm, which incorporates reverse learning, controlled elite, and dynamic crowding distance theory, is constructed to optimize the configurations of the proposed energy system, with the aim of minimizing carbon and economic prices of the products. The performance of the energy system is compared with different algorithms and cases, and the results demonstrate that the proposed algorithm addresses the limitations of conventional methods, albeit with increased optimization times. Moreover, the proposed operating mode (Case 1) exhibits a stable performance, leading to a higher environmental and economic performance compared to conventional modes, with carbon and economic prices of 0.242 kg/kWh and 0.067 $/kWh, respectively. This study provides new insights into carbon footprints and economic performance evaluation of high-efficiency energy systems, offering valuable directions for future research in this field. [Display omitted] • A solar-natural gas coupling system to fulfill building multiple demands. • Comparisons of fours cases considering operating sequences of two types of chillers. • Modified genetic algorithm using reverse learning, elite controlled, dynamic crowding distance theory. • Carbon and economic prices of the products are illustrated. [ABSTRACT FROM AUTHOR]

Sine Cosine Algorithm(SCA) is a population-based optimization algorithm, to find the optimal solution. However, SCA has problems such as premature convergence, insufficient solution precision for high-dimensional functions, and slow convergence speed. To solve the problems above, this paper proposes a multi-strategy-based Adaptive Sine Cosine Algorithm (ASCA). Firstly, a more uniform initial population is generated by the Halton sequence so that the initial population covers the entire search space to maintain the diversity of the initial population. Secondly, the adaptive grading strategy is adopted to sort according to the fitness value, and the population dynamics are divided into 4 grades: excellent, good, medium and poor. For the purpose of improving the convergence accuracy of the algorithm and enhancing the ability to jump out of the local optimum, hybrid mutation and elite guidance methods are applied to different levels of populations for perturbing mutations. Finally, in order to improve the convergence speed of the algorithm, a dynamic opposition-based learning global search strategy is proposed. The ASCA is tested on a set of 20 functions in low- dimensional and high-dimensional, and the improved algorithm is compared with Particle Swarm Optimization (PSO), Backtracking Search Algorithm(BSA), Genetic Algorithm(GA)and other improved Sine Cosine Algorithms. The results show the improved convergence accuracy and speed of the ASCA. Moreover, the ASCA proposed in this paper is applied to engineering optimization design. The solution results show that the ASCA is better than other algorithms in superiority-seeking ability, and can effectively solve the optimization problems in engineering. [ABSTRACT FROM AUTHOR]

• In this work, the influence of noise containing incoherent noise and coherent noise on ten wavefront shaping techniques is investigated by analyzing the focusing performances, and the noise influence to selecting operation or calibrating process. • We found that the algorithms with outstanding noise resistance ability similarly demonstrate high feedback signal change and low analytic errors, separable natural evolution strategies (SNES), genetic algorithm (GA), transmission matrix (TM) method, and partition algorithm (PA) are less noise sensitive approaches. • An improved GA with rapid convergence speed was presented and showed the same convergence speed as SNES. It is believed that, particularly, our proposed IGA assisted with advanced hardware optimization has great potential to accomplish dynamic focusing in complex scattering media such as biological tissues only with sub-seconds. • Our work provides a guidance on how to appropriately choose optimization algorithms and can be beneficial to future investigations related to high noise tolerant method in scattering focusing and imaging. Both enhancement and convergence speed are the most important metrics for wavefront shaping techniques on focusing light through scattering media, while the noise inherent in the experimental system always deviates the designed wavefront from the optimum. Recently, many techniques have been applied to overcome light scattering, such as element evaluation methods, populational pattern-based optimization methods, and transmission matrix (TM) method. However, only the focus performances under incoherent noise are generally considered, the influence from coherent noise and the noise resistance characteristics of the techniques are less considered. Here, we demonstrated the influence of incoherent noise and coherent noise on ten wavefront shaping techniques, the analytic errors occur in the selection operation or calibrating process by simulating, and experiments are implemented for four advantageous approaches. In particular, an improved genetic algorithm (IGA) with a fast convergence speed has been developed. Our studies not only provide valuable guidance on how to properly choose wavefront shaping techniques in actual environments, but also improve the focusing performance of the existing algorithms. [ABSTRACT FROM AUTHOR]

• Combined Simulated Annealing algorithm is developed to optimize the reload core loading pattern. • Combined Simulated Annealing algorithm couples the advantages of SA and the characteristic regularity in the optimization space. • Benchmarks calculation proves the optimization efficiency and optimization results of Combined Simulated Annealing algorithm. Reload loading pattern design is the key content to economy, safety and flexibility of the operation after reloading in nuclear power plant. Loading pattern optimization is a typical combinatorial optimization problem with multi-objective and multi-parameter. Furthermore, the reload loading pattern optimization design is a very short time requirement, which makes it difficult for traditional optimization algorithms. In this study , a combined simulated annealing algorithm (CSA algorithm) are proposed. CSA effectively utilizes the characteristic regularity in the optimization space. Additionally, the combined database created by CSA can significantly increase the diversity of the algorithm, and greatly improve the optimization ability. In comparison to the traditional simulated annealing method, the optimization results of CSA are noticeably better. By contrasting the CSA with the improved genetic algorithm (GA), it can be seen that CSA can exploit the characteristic regularity of the reload loading pattern optimization more effectively, and the optimization efficiency and optimization results of the method are noticeably superior to GA. [ABSTRACT FROM AUTHOR]

The paper presents a method that allows the thickness of a compact bone layer and longitudinal wave velocity in the bone to be determined simultaneously with the use of reflected waves, with particular emphasis on the case of layers when the layer thickness is of the same order as the ultrasonic wavelength. This paper presents the use of optimization methods for estimation the thickness of a layer using inverse method. The studied structure is a multilayer structure composed of two layers solid and porous material. To solve the inverse problem, a multi-layer model and ultrasonic signals obtained from cortical and cancellous bone phantoms by the reflection method was used. The genetic algorithm implemented in the Matlab® package Optimization Toolbox applied to find solution the optimization problem. The results of simulation and experiments demonstrate that the thickness of layer was obtained with good accuracy. [ABSTRACT FROM AUTHOR]

We proposed a dual-ejector-based regenerative dual–pressure organic flash cycle (DEj–RDPOFC) with higher performance than the RDPOFC. The effects of high and low pressures and the entrainment ratio of the ejector on the DEj–RDPOFC performance were analyzed from the viewpoints of thermodynamics and thermoeconomics. An exergy-flow distribution diagram revealed why the DEj–RDPOFC outperformed the RDPOFC. Next, the performances of the DEj–RDPOFC and dual–pressure organic Rankine cycle (DORC) were compared under the same boundary conditions. Finally, the performance of the DEj–RDPOFC was optimized using the nondominated sorting genetic algorithm (NSGA-II). According to the results, the power output of the high-pressure (HP) turbine of the DEj–RDPOFC can be increased by replacing the HP throttling valve (TV) with an ejector (which increases the working fluid dryness of the HP vapor separator and the mass flow rate of the HP turbine) and the low-pressure (LP) TV with an ejector (which increases the HP turbine expansion ratio). The DEj–RDPOFC achieves a higher maximum net power output (W net) and a lower corresponding levelized cost of electricity (LCOE) than the DORC. Under the optimum condition, the W net and LCOE of the DEj–RDPOFC are 9.71 % higher and 6.59 % lower, respectively, than those of the RDPOFC. • The effect of the HP and LP ejector on the performance of the regenerative DPOFC (RDPOFC) are analyzed. • The performance of the RDPOFC assisted by ejectors (DEj–RDPOFC) is compared with the dual-pressure ORC. • The DEj–RDPOFC is optimized using a multi-objective optimization algorithms. [ABSTRACT FROM AUTHOR]

The beam shaping assembly (BSA) plays a crucial role in the facility of accelerator-based boron neutron capture therapy (AB-BNCT). The quality of the neutron beam utilized for treatment is directly influenced by the design of the BSA. However, conventional step-by-step optimization approaches often encounter challenges in achieving the global optimal solution, as they tend to converge towards local optima. To address this issue, this study proposed an intelligent optimization method that combines neural network and genetic algorithm. The proposed method was applied to optimize a double-layered BSA based on the neutron source resulting from a lithium target bombarding by a 2.8 MeV, 20 mA proton beam. The obtained optimal BSA solution parameters well met the IAEA-TECDOC-1223 report, while also exhibiting significantly higher epithermal neutron flux compared to alternative BSA designs. The performance of the epithermal neutron beam was assessed by calculating the dose distribution and clinical parameters in the Snyder head phantom. The findings suggest that this beam exhibits promising therapeutic potential for treating tumors. [ABSTRACT FROM AUTHOR]

• Uses multi-objective optimization and Pareto optimality to monitor geologic carbon storage sites. • Incorporates user-supplied weighting criteria to prioritize detection of the most critical leakage scenarios. • Uses lightweight, efficient software design to run on a typical laptop or workstation. Designs for Risk Evaluation and Management (DREAM) is a tool developed under the National Risk Assessment Partnership (NRAP) to enhance geologic carbon storage safety and efficiency. Using potential leakage scenarios generated externally by the users preferred history-matching approach, DREAM constructs ideal combinations of sensor locations in the right place at the right time to detect as many leaks as possible, detect them as early as possible, and minimize cost. This user-friendly tool, developed in Java, features a window-based GUI for input and a 3D visualization tool for viewing the domain space and optimized monitoring plans. DREAM's latest version accommodates real-world usage by allowing for joint optimization of wellbore point sensor placements and surface geophysics survey geometries, and by using more efficient multi-objective optimization algorithms. In an example shown here, these two improvements combined allow us to support containment assurance and go from detecting 80–90 % of the potential CO 2 leakage to +99.7 %, a step-change improvement that can make the deciding difference in whether a site is suitable for geologic carbon storage. Though developed for geologic carbon storage, this tool would be equally applicable in many surface or offshore environmental monitoring projects. [ABSTRACT FROM AUTHOR]

Hybrid offshore renewable energy platforms have been proposed to optimise power production and reduce the levelised cost of energy by integrating or co-locating several renewable technologies. One example is a hybrid wave-wind energy system that combines offshore wind turbines with wave energy converters (WECs) on a single floating foundation. The design of such systems involves multiple parameters and performance measures, making it a complex, multi-modal, and expensive optimisation problem. This paper proposes a novel, robust and effective multi-objective swarm optimisation method (DMOGWA) to provide a design solution that best compromises between maximising WEC power output and minimising the effect on wind turbine nacelle acceleration. The proposed method uses a chaotic adaptive search strategy with a dynamic archive of non-dominated solutions based on diversity to speed up the convergence rate and enhance the Pareto front quality. Furthermore, a modified exploitation technique (Discretisation Strategy) is proposed to handle the large damping and spring coefficient of the Power Take-off (PTO) search space. To evaluate the efficiency of the proposed method, we compare the DMOGWA with four well-known multi-objective swarm intelligence methods (MOPSO, MALO, MODA, and MOGWA) and four popular evolutionary multi-objective algorithms (NSGA-II, MOEA/D, SPEA-II, and PESA-II) based on four potential deployment sites on the South Coast of Australia. The optimisation results demonstrate the dominance of the DMOGWA compared with the other eight methods in terms of convergence speed and quality of solutions proposed. Furthermore, adjusting the hybrid wave-wind model's parameters (WEC design and PTO parameters) using the proposed method (DMOGWA) leads to a considerably improved power output (average proximate boost of 138.5%) and a notable decline in wind turbine nacelle acceleration (41%) throughout the entire operational spectrum compared with the other methods. This improvement could lead to millions of dollars in additional income per year over the lifespan of hybrid offshore renewable energy platforms. • A new multiobjective optimisation method proposed and enhanced hybrid wave-wind platform. • A new combination of chaotic adaptive exploration and dynamic archive is introduced. • Effective and practical discretisation method improved performance of hybrid platform. • The performance of the proposed optimiser is validated on four real sea studies. [ABSTRACT FROM AUTHOR]

Pneumonia is a lung illness that may result from a variety of various viral diseases and may be lethal. It might be difficult to diagnose and treat pneumonia on chest X-ray pictures because pneumonia and other lung illnesses are closely connected. As a result, the present methods for forecasting pneumonia cannot achieve meaningful levels of accuracy. This study aids in identifying diseases from chest X-ray images that don't exhibit the same symptoms as pneumonia. Five stages will be included in the projected model: (a) pre-processing, (b) segmentation, (c) feature extraction, (d) optimal feature selection, and (e) detection. The gathered image is initially pre-processed using Dynamic Histogram Equalization (DHE), followed in this case by median filtering. At the segmentation phase, by utilizing the Enhanced Watershed Segmentation, the ROI is separated from the background region of the pre-processed image. Additionally, in the feature extraction step, features based on Local Gradient Increasing Pattern (LGIP), Harmonic Local Gradient Pattern (hLGP), Improved Local Ternary Pattern (ILTP), and Improved Local Gradient Pattern (ILGP) will be derived from the isolated ROI areas. Using the proposed AHGOA (Archimedes-assisted Henry Gas Optimization Algorithm) model, the best characteristics from these retrieved features were selected. This is the best option for avoiding the dimensionality curse. The selected EC + AHGOA method obtain good accuracy (∼0.95) for tuning percentage 70 in pneumonia diagnosis from Chest X-ray images than some other previous techniques includes EC + AOA (∼0.92), EC + HGSO (∼0.93), EC + HGS (∼0.88), EC + PRO (∼0.90), and EC + BES (∼0.89). [ABSTRACT FROM AUTHOR]

This paper explores the influence of different turbocharging modes and multi-parameter coordinated control on the performance of compression-ignition (CI) aviation piston engine (APE). Firstly, based on a constructed one-dimensional thermodynamic model of a CI APE, the study investigates the effects of different turbocharging modes and combinations of high and low-pressure stage variable geometry turbines (VGT) on the operational performance of the engine. Subsequently, a novel stepwise approximate multi-objective optimization algorithm is proposed, combining backpropagation neural networks and the non-dominated sorting genetic algorithm II. This algorithm evaluates the influence of multiple control parameters on a two-stage turbocharged engine's performance, achieving a balance between fuel economy and reliability. The research shows that equipping CI APEs with twin VGT for supercharging can notably enhance engine performance, enabling the achievement of favorable power recovery objectives at an altitude of 8000 m. The proposed optimization algorithm exhibits strong predictability and reliability, substantially accelerating the computation speed and reducing the data volume by approximately 95%. At an engine speed of 3887 rpm, compared to the unoptimized conditions, the brake specific fuel consumption of the best scenario at altitudes of 2000 m, 4000 m, 6000 m, and 8000 m is reduced by 7.1%, 7.2%, 8.6%, and 6.9%, respectively. • Influences of different turbocharging modes on the performance of a CI APE are investigated. • A novel stepwise approximate multi-objective optimization method based on a hybrid model is proposed. • Explored the altitude-varying control strategy under coordinated multi-parameter optimization. • Increased the altitude ceiling for the CI APE, reaching 82% power recovery target at 8000 m. [ABSTRACT FROM AUTHOR]

• Developing of a novel OST-NED optical design that uses a Mangin lens as a reflector. • A new automatic solution for solving the initial structure of the system. • Proposing of methods to solve the internal stray light and external light leakage. • The compact and lightweight system can achieve a large diagonal FOV of 50°. The way we interact with the world is constantly evolving thanks to developments in virtual reality (VR) and augmented reality (AR) technologies. AR has been extensively employed in a variety of fields and is one of the metaverse's key interfaces. It has always been difficult to develop and process an optical see-through (OST) AR with a wide field of view (FOV) that is also compact, lightweight, and low in stray light. In this study, we develop and build a high-brightness microdisplay-based polarized catadioptric AR near-eye display (NED) system with a birdbath (BB) structure. We suggest a unique optical design that uses a Mangin lens as a reflector and incorporates polarization elements into this system to address the issue of the classic BB AR NED system's inefficient use of energy. The optical and structural restrictions used in the design process are expressed mathematically. A solution for solving the initial structure using a genetic optimization algorithm is proposed based on this optical morphology. Using the resolved initial structure, the optimization design and image quality analysis procedure are detailed. The completed device has a 0.71-inch microdisplay with a diagonal FOV of 50°, an exit pupil diameter (EPD) of 8 × 6 mm, an eye relief (ERF) of 15.7 mm, and a maximum total length (TTL) of 15.4 mm. The system's paths and modes for generating stray light are examined, and the related techniques for reducing and eliminating stray light are provided. The system's prototype was finally processed successfully, confirming its great performance and adaptability. [ABSTRACT FROM AUTHOR]

Vortex-induced vibration will occur due to the vortex shedding around the marine towing cable during the towing movement, which will further affect the acquisition efficiency of the marine seismic exploration system. Suppression device is widely used to suppress the VIV response of the marine slenderness structures. In order to investigate the passive suppression mechanism on the marine towing cable, a numerical VIV model is proposed to predict the VIV response of marine towing cable with buoyancy modules. The steady-state and VIV characteristics of marine towing cable with different coverage, layout and slenderness ratio are studied firstly. The parameters have a nonlinear relationship with the VIV response of cable and excessive arrangement of buoyancy module will enlarge the vibration displacement. Where to install a limited number of suppression devices is a challenge for practical engineering. Genetic algorithm is introduced to solve the optimization problem of the suppression device arrangements in this paper. The suppression device arrangement of towing cable will evolve in the direction of decreasing cable VIV displacement until reaching the optimal arrangement. The proposed method is demonstrated by comparing with some typical working conditions. It turns out that the proposed method can perform the optimization design of the suppression device arrangement efficiently. The VIV displacement of cable can be reduced obviously through the optimization design. • A nonlinear optimization algorithm model is proposed to solve the optimization problem of marine towing cable. • Quasi-static configuration and nonlinear dynamic response characteristics with different buoyancy arrangements are analyzed. • A practical case considering several constraints is used to validate the effectiveness of the model. • The VIV displacement of cable is reduced obviously through the optimization design. [ABSTRACT FROM AUTHOR]

The automated berthing of vessels constitutes a high-risk operation. Equipping ships with azimuth and lateral thrusters enhances manoeuvrability, although it poses challenges in achieving optimal thrust allocation (TA) efficiency. This study introduces an improved adaptive non-dominated sorting genetic algorithm III (ANSGAIII) specifically designed to address the TA problem in autonomous ship berthing. The algorithm enhances the objective function by accounting for the nonlinear relationship between actual power consumption and thruster speed, along with considering the treatment of forbidden sectors while maintaining a high level of fidelity. Additionally, the algorithm considers equality constraints, actuator rate and position constraints, and other operational constraints. A modification to the non-dominated sorting method is implemented to improve the processing speed of the optimization algorithm. Significantly, this algorithm surpasses NSGAII, NSGAIII, and CMOEA\D in meeting ship safety and stability requirements while concurrently reducing energy consumption. • An ANSGAIII TA scheme is proposed to enhance berthing safety and energy efficiency. • Cost function and forbidden sector strategy are improved. • The non-dominated sorting method is refined to expedite the TA process. [ABSTRACT FROM AUTHOR]

Optimization of subsurface hydrocarbon production holds paramount importance for decision-makers as it determines crucial development strategies such as optimal well placement and well control parameters (e.g. injection/production rates of injectors and producers). Despite the availability of numerous established optimization methods in this field, traditional reservoir production optimization methods face challenges in simultaneously addressing multiple development objectives and coordinating the interaction of well control parameters in different control steps. In this work, we construct a hybrid artificial intelligence method to jointly optimize well placement and well control parameters, taking into account two development objectives and dynamic optimization. It consists of two stages. First, a reservoir potential map is generated with the production potential formula that considers reservoir pressure, remaining oil saturation and other reservoir properties (permeability, hydrocarbon column height) etc. The reservoir potential map provides guidance for placing well in medium to high potential areas and engineering constraints for the optimization process. Then, a hybrid artificial intelligence method that couples deep learning method (Long and Short Term Memory (LSTM)) and multi-objective optimization algorithm (Non-dominated Sorting Genetic Algorithm II (NSGA-II)) is established to seek a compromise between the two objectives in water-flooding processes. The LSTM neural network is trained as the surrogate model to replace the high-fidelity simulator to achieve high efficiency of overall optimization workflow. The NSGA-II algorithm is employed for handling the joint optimization problem of well placement and well control parameters by maximizing the cumulative oil production and minimizing the water cut. The performance of the proposed method is tested on one benchmark function and two reservoir models. On the 2D synthetic reservoir model the optimized scheme leads to a notable increase of 3 × 104 m3 in cumulative oil production, accompanied by 17 % reduction in water cut when contrasted with the base scheme. Similarly, within the 3D reservoir model, the optimized scheme results in a substantial enhancement, boosting cumulative oil production by 14 × 104 m3 and reducing water cut by 20% compared to the base scheme. Moreover, the proposed method surpasses alternative multi-objective optimization (MOO) algorithms by demonstrating 82% and 95% reduction in optimization time, respectively. The results demonstrate that this method can provide the optimal water-flooding strategies under the premise of different development objectives. The Pareto front (or optimal solutions) generated by the hybrid method offers a variety of diverse water-flooding strategies to assist subsurface engineers in making informed decisions. [ABSTRACT FROM AUTHOR]

In this paper, different optimization algorithms are introduced to solve the security-constrained optimal power flow between two connected regions, applying two control modes, namely the centralized and decentralized modes. Traditionally, the decentralized control, i.e., each power system controls its variables regardless of the boundary buses, is the most commonly used approach. However, the centralized control has been recently utilized to enhance the system security, because the active power of the generation units and/or the voltage buses on the boundaries are the two variables, which can mitigate the changes, when compared to the decentralized control solution. The optimization techniques applied propose using the Genetic Algorithm (GA) as a tool to validate the solution reached through the Whale Optimization Algorithm. The proposed methodology is investigated on the IEEE 48-bus electric power system, which consists of two regions connected to each other through three transmission lines. The problem can be solved by applying both the AC and DC Optimal Power Flow respectively, using three approaches, namely: 1- Changing the active power of generation unit; 2- Controlling the voltage magnitude and 3- Combining the changes in the real power of generation units and the voltage variations. All the proposed approaches are implemented taking into consideration the technical constraints. All of these different techniques are constructed and modeled by utilizing the Matlab code. The results of each case study are presented; and a final comparison between all the methods utilized is drawn to demonstrate the advantages of each method. The novelty of this paper is represented in investigating the effectiveness of applying the AC Optimal Power Flow and the DC Power Flow, utilizing meta-heuristic techniques. [ABSTRACT FROM AUTHOR]

Population-based optimization algorithms are popular tools for optimization, influenced by two interactive factors: landscape bias, which guides the population toward better objective function values, and structural bias, which directs the population to a specific location in the search domain. Structural bias can impede the exploration of algorithms, compelling the population to revisit a particular area of the search domain, which leads to increased computing costs without yielding any new information. In this study, we present a comprehensive survey of structural bias. Identifying this bias in a general algorithm is challenging. We propose the 'Generalized Signature Test' as a methodology to detect and analyze the structural bias in population-based optimization algorithms. Numerical simulations are performed using six well-known metaheuristic algorithms: Genetic Algorithm, Differential Evolution, Particle Swarm Optimization, Grey Wolf Optimizer, Whale Optimization Algorithm, and Harris Hawks Optimization. Experimental results suggest that Differential Evolution displays the least bias toward the center of the search domain, whereas the Harris Hawks Optimization exhibits a significant axial-diagonal bias toward the origin. Our findings indicate that the proposed method is both efficient and straightforward for assessing algorithmic structural bias. The Generalized Signature Test provides an in-depth analysis and understanding of biases, tailor-made for thorough bias behavior investigations. The Generalized Signature Test can identify bias in established algorithms and evaluate bias in emerging algorithms at their early development stages. [ABSTRACT FROM AUTHOR]

Traditional heuristic optimization algorithms are no longer applicable to the multiple satellites scheduling with large-scale tasks, as they are unable to provide satisfactory performance in terms of convergence and speed when addressing complex constraint conflicts. Therefore, we propose a multi-satellite cooperative scheduling method for large-scale tasks based on a hybrid graph neural network (GNN) and metaheuristic algorithm. By using the representation and extraction capability of GNN for relations in graph, the features of large-scale tasks and their constraint relations were expressed, and the generalized knowledge was extracted. And combined with metaheuristic algorithm, the optimization framework for large-scale satellite task scheduling based knowledge and data was implemented. The method consists of a GNN pre-solver module for static constraint conflicts and a metaheuristic optimization module for dynamic constraint conflicts of satellite missions. In the GNN pre-solver module, the graph sample and aggregate network was used to learn and extract the common knowledge in the static task-conflict graph, and provided effective prior information for the metaheuristic optimization module. The quadratic unconstrained binary optimization loss function was designed with multiple influencing factors, to convert the discrete optimization function into a continuous function; a greedy threshold was also added to improve the task completion rate. Finally, numerical experimental results showed that the proposed method can achieve an efficient solution to the multi-satellite scheduling problem with tens of thousands tasks. Compared with the commonly used multi-satellite scheduling algorithms (the genetic algorithm (GA) and greedy-GA), the proposed method can obtain higher-quality solutions under the same conditions and greatly improve the computational efficiency of large-scale mission planning. [ABSTRACT FROM AUTHOR]

This study introduces a novel approach utilizing Particle Swarm Optimization (PSO) to determine the optimal mix of renewable and non-renewable energy sources specifically tailored for Romania. Relying on data from January 2019 to August 2022, this research ascertains the most efficient combination of energy sources, considering cost, generation capacity, and environmental impact. PSO is compared with six other optimization algorithms: grey wolf optimizer, genetic algorithm, harmony search, mayfly algorithm, flower pollination algorithm, and CUKO search. The following mix is proposed: coal 0 %, oil & gas 2.74 %, hydro 18.92 %, nuclear 12.20 %, wind 24.05 %, solar 26.99 %, and biomass 15.09 %. • In order to ensure energy security and mitigating climate change, optimizing the energy mix becomes imperative. • The optimal mix of renewable and non-renewable energy sources can be design using Particle Swarm Optimization (PSO) method. • A multi-objective PSO model balances key aspects such as environmental impact, economic feasibility, and energy security. • This study introduces a novel approach utilizing PSO to determine the optimal energetic mix specifically tailored for Romania. [ABSTRACT FROM AUTHOR]

The optimal power flow (OPF) is one of the most noticeable and integral tools in the power system operation and control and aims to obtain the most economical combination of power plants to exactly serve the total demand of the system without any load shedding or islanding through adjusting control variables to meet operational, economic, and environmental constraints. To achieve this goal, the successful implementation of an expeditious and reliable optimization algorithm is crucial. To solve this issue, this research proposes an enhanced democratic political algorithm (DPA), which can effectively solve multi-objective optimum power flow problems. The proposed method is a version of the democratic political optimization algorithm in which the search capability of this method to cover the borders of the Pareto frontier is enhanced. For the sake of practicality, the objectives with innate differences such as total emission, active power loss, and fuel cost are selected. Due to the practical limitations in real power systems, additional restrictions including valve-point effect, multi-fuel characteristics, and forbidden operational zones, are also considered. The proposed approach is tested and validated on IEEE 57-bus and IEEE 118-bus systems with different case studies. Simulation results are analyzed and compared with two popular and commonly used multi-objective-evolutionary algorithms namely, non-dominated sorting genetic algorithm II (NSGA-II) and the multi-objective particle swarm optimization (MOPSO) on the problem. The study results illustrate the effectiveness of the proposed method in handling different scales, non-convex, and multi-objective optimization problems. [ABSTRACT FROM AUTHOR]

Expensive objectives and constraints are key characteristics of real-world multi-objective optimization problems. In practice, they often occur jointly with inexpensive objectives and constraints. This paper presents the Inexpensive Objectives and Constraints Self-Adapting Multi-Objective Constraint Optimization algorithm that uses Radial Basis function Approximations (IOC-SAMO-COBRA) for such problems. This is motivated by the recently proposed Inexpensive Constraint Surrogate-Assisted Non-dominated Sorting Genetic Algorithm II (IC-SA-NSGA-II). These algorithms and their counterparts that do not explicitly differentiate between expensive and inexpensive objectives and constraints are compared on 22 widely used test functions. The IOC-SAMO-COBRA algorithm finds significantly better (identical/worse) Pareto fronts in at least 78% (6%/16%) of all test problems compared to IC-SA-NSGA-II measured with both the hypervolume and Inverted Generational Distance+ performance metric. The empirical cumulative distribution functions confirm this advantage for both algorithm variants that exploit the inexpensive constraints. In addition, the proposed method is compared against state-of-the-art practices on a real-world cargo vessel design problem. On this 17-dimensional two-objective practical problem, the proposed IOC-SAMO-COBRA outperforms SA-NSGA-II as well. From an algorithmic perspective, the comparison identifies specific strengths of both approaches and indicates how they should be hybridized to combine their best components. [Display omitted] • Self-adaptive optimization algorithm for Parallel Constraint Multi-Objective problems. • Optimization algorithm for a mix of Expensive and Inexpensive functions. • Direct use of Inexpensive functions in the optimization algorithm. • Extensive comparison between IC-SA-NSGA-II and IOC-SAMO-COBRA optimization algorithm. • Real-world cargo vessel design has been optimized with IOC-SAMO-COBRA. [ABSTRACT FROM AUTHOR]

• Microchannel heat sink with cavities and longitudinal vortex generators is proposed and evaluated based on three-dimensional numerical simulation. • The main effects of cavities and longitudinal vortex generators geometric parameters on thermal resistance and pumping power of the microchannel heat sink are studied. • The optimal results based on the primary geometric parameters are obtained by the multi-objective optimization algorithms. • The optimal solutions from 0.2594 K/W to 0.4176 K/W for thermal resistance and 0.0117 W to 0.047 W for pumping power, respectively. Microchannel heat sink (MCHS) is an innovative cooling device for high-powered integrated circuits. Cavities and longitudinal vortex generators (LVGs) are two kinds of effective heat transfer enhancement techniques for MCHS. It has been demonstrated that cavities can enhance the heat transfer but generate the dead zone of flow, while longitudinal vortex generators (LVGs) have the advantage of low pressure drop. In this paper, an integrated microchannel heat sink combing cavities and LVGs (MC-LVG-C) is proposed. The impacts of geometric parameters of cavities and LVGs on the thermal-hydraulic performance of proposed MCHSs are analyzed and evaluated by CFD when the Reynolds number (Re) ranges from 150 to 325. Further, the multi-objective configuration optimization based on Non-dominated Sorted Genetic Algorithm-II (NSGA-II) is performed at Re= 325 to minimize the thermal resistance and pumping power of MC-LVG-C simultaneously. It is found that the proposed scheme (MC-LVG-C) has clear superiority in the thermal-hydraulic performance over MCHS with cavities only (MC-C) and MCHS with LVGs only (MC-LVG). Geometric parameters analysis reveals that the height (h l) and length (L) of LVGs and the spacing (L 2) between LVGs and cavities have significant impact on the thermal-hydraulic performance. The NSGA-II optimization indicates that optimal thermal-hydraulic performance values for MC-LVG-C are divided into four design zones. For optimal solutions, better heat transfer can be obtained by increasing h l and L , while smaller resistance can be provided by increasing h l and L 2. In practical design, the reasonable geometric parameters can be chosen from the proposed four design zones by the design requirement. [ABSTRACT FROM AUTHOR]

This work deals with the ship multi-deck cabin layout (SMCL) problem, aiming to provide insights for the application of intelligent design techniques in the field of shipbuilding. Based on decomposing SMCL into the problem of cabin allocation on multiple decks and the problem of cabin arrangement on each deck, the improved Systematic Layout Planning (SLP) method is adopted to automatically allocate cabins on multiple decks, and then the improved Genetic Monarch Butterfly Optimization Algorithm (GMBOA) is used to optimize the layout of the cabins allocated on each deck. An optimization model of SMCL is created, including a simplified model of the SMCL and five optimization objective functions. The improved GMBOA is established by introducing the crossover and mutation operations of genetic algorithm into the monarch butterfly optimization algorithm and optimizing the butterfly's migration algorithm to solve the cabin layout problem; at the same time, the Grefenstette coding method is used to avoid the destruction of cabin sequences by crossover and mutation operations. Finally, by comparing the numerical simulations on a tanker and a passenger ship with the original design, this study demonstrates the effectiveness and robustness of the proposed method in providing innovative and reasonable solutions. • This paper proposes an improved SLP method and improved genetic monarch butterfly optimization algorithm (GMBOA) for intelligent ship multi-deck cabin layout design. • The improved SLP method solves the allocation problem of ship cabins on multi-level decks by introducing vertical layout improvements into the SLP method. • The improved GMBOA adapts to the solving needs of cabin layout problems by introducing the crossover and mutation of the genetic algorithm into the MBO algorithm. • The optimization model of ship multi-deck cabins layout is established. [ABSTRACT FROM AUTHOR]

This paper presents an improved Teaching–Learning-Based Optimization Algorithm for controlling a UAV swarm to achieve real-time monitoring of indoor air quality and possible pollution source location search. The improvements to the Teaching–Learning-Based Optimization Algorithm focus on the following four points: proposing a variable teaching factor to improve search speed and accuracy, establishing an individual capability value model to integrate real-world UAV status with the algorithm, increasing the number of teachers and providing updated formulas to improve the algorithm's convergence speed, and proposing a grouping strategy to achieve real-time monitoring of indoor air quality and pollution source search within a unified algorithm framework. At the end of the article, possible legal challenges for deploying this algorithm are also discussed. • Improved TLBO and its use for UAV control in air composition testing. • Further consideration of the UAV's internal state to improve multi-UAV algorithm. • Obtained an algorithm for localizing gas sources in indoor environments. [ABSTRACT FROM AUTHOR]

A Supervised Parallel Optimisation (SPO) is presented. The proposed framework couples different optimisation algorithms to solve single-objective optimisation problems. The supervision balances the exploration and exploitation capabilities of the distinct optimisers included, providing a general framework to solve problems with diverse characteristics. In this work, five optimisation algorithms are included in the ensemble: Particle Swarm Optimisation (PSO), Genetic Algorithm (GA), Covariance Matrix Adaption-Evolution Strategy (CMA-ES), Differential Evolution (DE), and Modified Cuckoo Search (MCS). A geometric path-finding problem with numerous local minima is used to demonstrate the advantage of SPO. The effectiveness of the approach is compared with that of stand-alone incidences of the integrated optimisation strategies and with state-of-the-art algorithms. In addition, a benchmark test suit composed of engineering applications is utilised to validate the general applicability of SPO with respect to a variety of problems. The good solutions generated by SPO are shown to be generally reproducible, while isolated algorithms, at best, render good solutions only occasionally. • A supervised parallel optimisation (SPO) ensembles distinct optimisers. • SPO balances exploration/exploitation to solve diverse problems. • Extensive tests based on SPO ensemble using optimisers PSO, GA, CMAES, DE, MCS. • SPO outperforms isolated algorithms finding reproducible solutions. • The strategy reduces the necessity of exhaustive hyperparameter fine-tuning. [ABSTRACT FROM AUTHOR]

Estimating gas source terms is essential and significant for managing a gas emission accident. Optimization method, as a kind of estimation methods, is helpful to figure out the source terms by solving the inverse problem. Significantly, the performance of optimization method on source term estimation is affected by the accuracy of forward dispersion model. To enhance the estimation accuracy, previous works have demonstrated the feasibility of using Back Propagation Neural Network (BPNN) trained by actual experimental datasets as a forward dispersion model. However, the overall accuracy of source estimation is still limited by backward estimation methods. Most related studies used a single optimization algorithm to estimate source terms, which usually fails to realize the requirements of both high calculation accuracy and satisfying computational efficiency. Therefore, a hybrid strategy was proposed in this study to combine optimization algorithms with different characteristics, including particle swarm optimization, genetic algorithm and simulated annealing algorithm, to not only achieve high accuracy in global searching, but also converge to a stable result efficiently. Finally, extensive experiments are conducted to testify our proposed hybrid optimization algorithms. The Skill scores of hybrid optimization algorithms decrease obviously compared to those of single optimization algorithm. Hence, the proposed hybrid strategy is potentially useful for guiding the combination of optimization algorithms for gas source terms estimation, which further contributes to deal with a gas emission accident with satisfying calculation accuracy and computational efficiency. [ABSTRACT FROM AUTHOR]

[Display omitted] This paper presents the parameter identification procedure of carbon nanotubes (CNTs) reinforced interphase in fuzzy fibre reinforced polymer (FFRP). The procedure was completed with ANSYS Workbench 19.2 software by combining Mechanical ADPL and Goal-Driven Optimisation. Firstly, a three-phase representative volume element (RVE) containing carbon fibre, CNTs reinforced interphase and epoxy resin was developed as a collection of Mechanical APDL commands. This RVE model was simulated to evaluate the elastic constants of FFRP lamina. CNTs reinforced interphase was characterised by transversely isotropic model. Interphase properties were parametrised and became input parameters in the Goal-Driven Optimisation. FFRP lamina elastic constants were set as the output parameters. Multi-objective Genetic Algorithm (MOGA) was used to identify the interphase properties, so that the output FFRP lamina elastic constants match the objective and constraints. The optimisation algorithm converged after 585 evaluations. Five potential candidate point, which met required objectives and constraints, were found. The identified interphase properties agreed well with the literature (an average percentage error of around 2%). This inverse procedure shows the potential to identify the interphase properties in nano-engineered composites, which are extremely difficult to measure experimentally. [ABSTRACT FROM AUTHOR]

Photodiode equivalent circuit models often involve numerous parameters, making the parameter extraction process complex. Therefore, this work presents a hybrid genetic algorithm (HGA) to extract photodiode parameters. Firstly, we deduce the calculation formula of the scattering parameters of the photodiode equivalent circuit model. Then, to address the limitations of the standard genetic algorithm (SGA) that its slow convergence and extended search times, we incorporate the elite strategy and acceleration operator, enhancing the local search ability of HGA. We compare the performance of the HGA with the other four optimization algorithms under multiple sets of measured data. Results show that HGA has better performance in parameter fitting, convergence speed, and accuracy from 10 MHz to 40 GHz, highlighting its superiority. The algorithm significantly simplifies photodiode parameter extraction, and provides a feasible solution for the parameter extraction of optoelectronic devices. [ABSTRACT FROM AUTHOR]

Antennas with a cosecant-squared pattern play a significant role in air-surveillance radar systems. In this article, a cosecant-squared pattern leaky-wave antenna (LWA) is synthesized using a ridge waveguide. The LWA consists of periodic shunt slots located at the broad wall of a ridge waveguide. To obtain the desired cosecant-squared pattern, the attenuation and the phase constant must be controlled properly along the antenna length. To this end, the slots offset and the ridge profile are properly tapered along the antenna. This adjustment is performed through an optimization algorithm such as a genetic algorithm (GA) optimization. Through the proposed antenna configuration, a cosecant-squared pattern LWA with a wide range of 10° to 40° together with low ripples (≤2 dB) in the shaped region and a low level of sidelobes (≤−18 dB) is synthesized, fabricated and measured. Apart from the good agreement between the simulation and the measurement results, such a design indicates a size reduction (about 47%) compared with the previous published work. [ABSTRACT FROM AUTHOR]

• Numerical heat exchanger (HE) approximations can cause optimizations to fail. • Novel hybrid high and low-order interpolation improve on traditional approaches. • Around 10 grid points should be used for each HE in an optimization study. • Hybrid interpolation methods are 5–10 times faster than conventional methods. • Sequences of non-linear constrained gradient-based optimizations should be used. Heat exchangers (HEs) are often modeled using pinch point temperature difference (Δ T pinch) methods when optimizing systems with HEs. However, even small inaccuracies in model predictions of HEs will introduce numerical noise that can cause optimization algorithms to fail. A recent study of single HEs suggests that high-order interpolation methods can compute Δ T pinch much faster than conventional methods. However, the performance of such methods when optimizing HE systems have not previously been tested. Heat pumps with 2 and 3 HEs, with and without an ejector are optimized using different schemes. Results from these case studies show that non-linear constrained gradient-based optimization algorithms are more than 5 times faster than particle swarm (PS), and that the conventional genetic algorithm (GA) should not be used. However, the main conclusion is that the case study optimizations are solved 5–10 times faster if Δ T pinch is calculated using hybrid high and low-order interpolation methods. [ABSTRACT FROM AUTHOR]

Offshore large-scale cluster extended reach wells (ERWs) are widely used to develop offshore oil & gas resources. Due to the complex downhole environments and complicated geological conditions, drilling parameters real-time optimization is challenging in offshore large-scale cluster ERWs drilling. In this paper, a real-time drilling parameters optimization method for offshore large-scale cluster extended reach drilling based on an intelligent optimization algorithm and machine learning (IOA-ML) is proposed. The method takes ROP as the objective function, establishes a ROP model based on long short-term memory (LSTM) neurons, and obtains drilling parameters optimization results asynchronously by combining the genetic algorithm, differential evolution algorithm, and particle swarm algorithm. The results show that ROP has been improved by 33.33% on average after the optimization by this real-time intelligent optimization method with an optimization time within 60s, which meets the requirements of real-time optimization of drilling parameters. • A real-time drilling parameters optimization method for ERWs drilling is proposed. • Parameters of offshore large-scale cluster ERWs drilling are optimized based on IOA-ML. • The method meets the requirements of real-time optimization of drilling parameters. [ABSTRACT FROM AUTHOR]

A distribution system's network reconfiguration is the process of altering the open/closed status of sectionalizing and tie switches to change the topological structure of distribution feeders. For the last two decades, numerous heuristic search evolutionary algorithms have been used to tackle the problem of network reconfiguration for time-varying loads, which is a very difficult and highly non-linear efficiency challenge. This research aims to offer an ideal solution for addressing network reconfiguration difficulties in terms of a system for power distribution, to decrease energy losses, and increase the voltage profile. A hybrid Genetic Archimedes optimization technique (GAAOA) has also been developed to size and allocate three types of DGs, wind turbine, fuel cell and PV considering load variation. This approach is quite useful and may be used in many situations. This technique is evaluated for loss reduction and voltage profile on a typical 33-bus radial distribution system and a 69-bus radial distribution system. The system has been simulated using MATLAB software. The findings suggest that this approach is effective and acceptable for real-time usage. [ABSTRACT FROM AUTHOR]

• A distributed-adaptive genetic algorithm is developed to perform the structural design of FTMCs. • Different types of novel FTMCs are optimally designed in a rectangular heat sink to achieve the highest COP. • The optimized FTMCs show a COP enhancement of around 0.09 ∼ 0.57 compared to the optimized conventional RPMCs. • The enhancement mechanism of FTMCs is discussed in present work. Recently, an innovative heat dissipation technique of fractal-tree structures has attracted attention due to its excellent high heat transfer efficiency and low pumping power. Though the impact of geometrical parameters such as branching level and dimension ratios of successive branches on the heat transfer efficiency and pumping power are considered to be critical, the impact mechanisms are still not well studied and formulated. Hence, there is still no clear method to optimally arrange the fractal-tree microchannels (FTMCs) in a rectangular heat sink to achieve better thermal and hydraulic performance. Therefore, we developed a multi-objective optimization algorithm (distributed-adaptive genetic algorithm, DAGA) to optimize the different types of novel FTMCs concerning achieving an optimal COP (coefficient of performance, heat transfer/ pumping power). The developed DAGA can improve the computational efficiency and the quality of optimum by around 87.63% and 12.48%, respectively in the present work. Then, taking conventional rectangular parallel microchannels (RPMCs) as a reference, the COP of the optimized FTMCs in the same heat sink shows an enhancement of around 0.09 ∼ 0.57. The highest COP is achieved by the three-level branching FTMC. Furthermore, the optimized results reveal that branching level, bifurcation number, and microchannel width at the first branching level are sensible, while dimension ratio factors and microchannel length at the first branching level show low sensibility to the COP of FTMCs. [ABSTRACT FROM AUTHOR]