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Multi-objective optimization algorithms are widely employed in the optimization of Dividing-Wall Column (DWC). However, due to the inherent complexity of DWC design problems, the choice of different optimization algorithms significantly influences the results. This study aims to investigate the applicability of various multi-objective optimization algorithms in the design of ternary DWCs. Three multi-objective algorithms including multi-objective genetic algorithm (MOGA), multi-objective differential evolution with tabu list algorithm (MODE-TL), and multi-objective particle swarm algorithm (MOPSO) are used to solve the optimization problem of six configurations of dividing-wall columns including direct dividing-wall column, indirect dividing-wall column, Petlyuk dividing-wall column, and three configurations of liquid-only transfer dividing-wall column (LTS), and the optimization results are compared and analyzed. The results show that MOPSO has the worst performance and is relatively less applicable to dividing-wall columns. The MOGA has great global exploration ability but is sensitive to structure complicity. The MODE-TL algorithm can make balance between exploration and exploitation and is less sensitive to structure complicity and the number of variables. Finally, according to the comprehensive performance of the three algorithms, we conclude that MODE-TL has the highest applicability in ternary dividing-wall column optimization. [Display omitted] • Six alternative ternary DWCs, including liquid-only transfer DWCs, are designed and optimized. • The performance of three improved optimization algorithms in six configurations is compared and the reasons are analyzed. • The most applicable multi-objective algorithm for ternary DWCs' optimal design is discussed. [ABSTRACT FROM AUTHOR]

• Differential privacy and deep learning are combined to alleviate the problem of model privacy leakage. • Differential evolution is used to optimize the learning rate, accelerate the model convergence, and avoid the privacy loss. • The particle swarm optimization method is used to find the optimal weight that satisfies the privacy. • The algorithm in this paper is verified on three data sets, and the accuracy and efficiency are better. In deep learning differential privacy protection, adding noise based on gradient has become a mainstream algorithm, but excessive gradient perturbation noise causes accuracy degradation. To solve this problem, a differential privacy protection algorithm based on differential evolution and particle swarm optimization is proposed to realize hyperparameter optimization in differential privacy, reduce the impact of noise on the model, and effectively improve the accuracy. On the one hand, the differential evolution scheme performs selection, crossover and mutation on learning rate η , make it approach the global optimal solution, and improve the computational efficiency of the algorithm. On the other hand, the particle swarm optimization scheme is adopted. Without changing the parameters and gradient structure, the parameters are optimized by using the network propagation attributes, which reduces the influence of noise on the accuracy. Experiments are performed on three datasets: Cifar10, Mnist and FashionMnist. Compared with the existing differential privacy algorithms, under the same privacy budget, the proposed algorithm has better accuracy and higher efficiency. [ABSTRACT FROM AUTHOR]

[Display omitted] • Experiments guided by the model achieved degradation rates of 93% and 92%. • The predicted degradation rate deviated by only 3% from experimental outcomes. • The classification model pinpointed key degradation contributors. • The regression model efficiently pinpoints optimal experimental conditions. • Integrating both models notably reduces the time required for experimentation. In this research, the XGBoost (XGB) and CatBoost (CB) algorithms were utilized to examine the experimental refinement and deployment of bimetallic catalysts for the activation of peroxymonosulfate in the removal of fluoroquinolone antibiotics from aqueous solutions. For the XGB model, shapley additive explanations analysis was utilized to provide model interpretability, identifying the features with significant impact on model prediction. By integrating multiple optimization techniques, such as particle swarm optimization and differential evolution, we successfully determined the optimal feature combination for the XGB model, providing accurate guidance for degradation experiments. Under the designed experimental conditions, the two sets of materials could degrade 93% of ciprofloxacin and 92% of norfloxacin within 30 and 27 min, respectively, with the discrepancies from model-predicted outcomes all within 3%. Concurrently, through electron paramagnetic resonance testing and quenching experiments, we explored the role of reactive oxygen species in the catalytic degradation process, with the experimental evidence demonstrating a 100% prediction accuracy for the CB model. The experimental results underscored the substantial potential of machine learning in the optimization design of environmental engineering. [ABSTRACT FROM AUTHOR]

Network slicing enables diverse scenarios within The Fifth Generation of Cellular System (5G) and beyond systems. Mapping virtual networks to their physical counterparts, known as the Virtual Network Embedding (VNE) problem, is challenging with mathematical complexities. While Linear Programming (LP) approaches can address the VNE problem, they may prove impractical due to the execution time required to obtain optimal solutions. Furthermore, meta-heuristic algorithms offer a promising alternative in balancing solution optimality and execution time. In this study, we propose the Artificial Algae Algorithm (AAA) as a solution for the VNE problem in network slicing scenarios specific to 5G. We compare the performance of AAA with other commonly used meta-heuristics, including Genetic Algorithm (GA), Differential Evolution (DE), and Particle Swarm Optimization (PSO). Since the evolutionary process of AAA consistently operates in three random dimensions, irrespective of the problem's dimensionality, the runtime performance of AAA remains unaffected by the number of virtual nodes, yielding execution times up to ten times faster than DE and PSO when considering 30 nodes. In that scenario, the proposed approach with AAA presented an improvement of more than 60% compared to DE and PSO in an execution time ten times lower. [ABSTRACT FROM AUTHOR]

A modern power system network is more susceptible to oscillatory instability problem due to power oscillations caused by uncertain penetration of renewable sources. Also, pumped storage hydro power generation (PSHPG) is going to take important role for energy storage and management with varying renewable penetrations, but their governors need to be utilized efficiently for power oscillation damping. In this work a coordinated modified pumped storage governor with fractional parallel unified power flow controller (FPUPFC) has been modelled and controlled to damp angular frequency variations in power system. The prime objective of this work is to enhance efficacy of pumped storage hydro turbine governors integrated into an expert system with UPFC to take part in electromechanical oscillation damping. Here the governor is modified by fractional order PID action with additional phase compensation and in FPUPFC both series and shunt converters are controlled simultaneously by parallel fractional lead-lag based control action. The power system considered here consists of hydro generators of 100 MVA with 90.94 MW rated turbine output power with different operating conditions for two area 4 generators and 39 bus 10 generators multimachine renewable integrated system. It has been observed that efficacy of pumped storage hydro turbine governor action has been much improved when coordinated with FPUFC control action. A new hybridized modified differential evolution with fractional particle swarm optimization (MDEFPSO) is proposed to tune the parameters of proposed controller, with a multi-objective function considering variation in speed and real power deviations, settling time, overshoot and undershoot of system response. • Modelling of pumped storage governor with unified power flow controller proposed. • A fractional parallel control action is proposed. • Coordinated fractional pumped storage governor based damping controller proposed. • A new hybridized algorithm is proposed to tune controller gains. • Stochastic renewable penetrations being analysed with proposed control action. [ABSTRACT FROM AUTHOR]

The paper aims to solve the problem of multi-agent path planning in complex environment using optimization algorithm. To address the issue of local optimum and premature convergence, a new method is proposed based on the whale optimization algorithm, combining the chaotic initialization, the reverse search and the differential evolution methods. It is theoretically proved that this algorithm is globally convergent in probability. When applied to path planning problems, the proposed optimization algorithm can effectively find a globally optimal and smoother path. Through simulation experiments with multi-UAVs, it is demonstrated that the proposed algorithm has better performance than the state-of-the-art methods in environment with both static and dynamic obstacles, reflecting the global convergence and robustness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

• Formulation of a perishable inventory model in multi-echelon distribution systems. • Integration of surrogate models significantly improves the performance of particle swarm optimization and differential evolution algorithms. • Development of a co-evolutionary strategy for efficient data point sampling of surrogate models in differential evolution algorithm. • The proposed algorithm outperformed 12 different metaheuristics. • Introduction of a new performance indicator to analyze the mechanism of surrogate-assisted evolutionary computation techniques. Simulation is widely used for analyzing supply chains with complex structures and stochastic nature. However, optimizing supply chain simulation models is usually computationally expensive. This study proposes a surrogate-assisted evolutionary optimization approach to optimize the inventory policies in multi-echelon distribution systems for perishable items under a limited number of evaluations. The random forest algorithm is used to build the surrogate model for a faster estimation of the performance of the inventory policies. A co-evolutionary differential evolution algorithm is proposed to simultaneously evolve the population through multiple searching strategies. The generated solutions are estimated by the low-cost surrogate model to select the promising solutions, which will be evaluated by the inventory model. Moreover, this study also integrates the surrogate model into two classic metaheuristic algorithms, particle swarm optimization and differential evolution. Also, a new performance indicator is proposed to examine the efficiency of the surrogate model in evolutionary computation. Two case studies are used to investigate the performance of the proposed algorithms. The experimental results show that both particle swarm optimization and differential evolution exhibit performance improvements exceeding 55% by using surrogate models under the limited number of function evaluations. Furthermore, the surrogate model reduces computational time for both algorithms by over 34% to achieve equivalent objective values. Finally, the proposed co-evolutionary differential evolution algorithm is compared with 12 algorithms, and the results show that the proposed algorithm consistently outperforms them. These findings confirm the usefulness of the surrogate model in evolutionary algorithms and the effectiveness of the proposed co-evolutionary strategy for solving perishable inventory problems. [ABSTRACT FROM AUTHOR]

• Propose and proved simplified representation of multi-threshold Tsallis entropy and Renyi entropy. • Proved that Tsallis entropy and Renyi entropy are equivalent for multi-threshold segmentation with the same parameters. • Proposed recursive algorithm of simplified Tsallis entropy and Renyi entropy under multiple thresholds. • Compared with the traditional Tsallis entropy, the time complexity of the simplified representation is lower, and the time complexity of the recursive simplified representation is the lowest. Image segmentation is an important step in obtaining image information, and it has always been an extremely critical link in the fields of computer vision and pattern recognition. Because of its simplicity and effectiveness, threshold-based image segmentation technology has received significant attention and research by researchers. Tsallis entropy and Renyi entropy are two important global threshold selection methods in image thresholding. They not only have a special correspondence in mathematical expression, but also have a certain relationship in the field of image segmentation. Considering the number of thresholds increases, the complexity of the expression of Tsallis entropy criterion function also increases sharply, which is difficult to apply in multiple thresholds. Given this, this paper proposes a simplified expression of Tsallis entropy with multiple thresholds, which is proved by mathematical induction. Simultaneously, it is concluded that the multiple-threshold segmentation of Tsallis entropy and Renyi entropy with the same parameters is equivalent. To overcome the inconvenience of high calculation cost under multi-threshold and to improve calculation efficiency, recursive algorithms based on simplified Tsallis entropy and Renyi entropy under multi-threshold are given. The calculation costs of the traditional Tsallis entropy and Renyi entropy threshold, simplified Tsallis entropy and Renyi entropy threshold, and the recursive simplified Tsallis entropy and Renyi entropy are compared through experiments. The results have revealed that the calculation time of simplified Tsallis entropy and Renyi entropy is shorter than the traditional methods, and the calculation time of the recursive simplified Tsallis entropy and Renyi entropy is much lower than that of the simplified expression. Finally, we use the three optimization algorithms including particle swarm, differential evolution and Harris hawks algorithm to solve the optimal solutions of the six expressions in the case of multilevel thresholds. Experiments also show that recursive simplified Tsallis entropy and Renyi entropy have the lowest calculation time cost under the same parameters. [ABSTRACT FROM AUTHOR]

The optimization of hydropower multi-reservoir systems is one of the most challenging reservoir management problems due to the complex dynamic nonlinear influencing variables such as reservoir active storage volume, released water discharge rate, and water pressure head available for hydropower generation. To optimize such a complex problem in an efficient way, the development of a high-precision meta-heuristic algorithm are of essential significance. This paper develops an adaptive differential evolution with particle swarm optimization (A-DEPSO) algorithm to derive optimal operating rules for multi-reservoir systems with the purpose of hydropower production. A-DEPSO algorithm uses novel mutation and crossover mechanisms to improve global and local search capabilities, respectively. The application of the proposed method is verified to solve a complex four-reservoir hydropower generation system, located in the southwest of Iran. The results achieved indicate that the A-DEPSO reduced the value of the objective function by an average of 57% compared with other well-known optimizers in the literature. Furthermore, the total power generated from the four-reservoir system using the proposed method increased by an average of 11% compared to the other methods. Finally, the results obtained in this study confirm that the A-DEPSO can be used efficiently to solve complex multi-reservoir hydropower generation systems. • An adaptive DE with PSO (A-DEPSO) is proposed. • A novel mutation and crossover operator are introduced. • The performance of the A-DEPSO is evaluated by a hydropower 10-reservoir problem. • A-DEPSO is used to optimize the operating rules for a hydropower four-reservoir system. [ABSTRACT FROM AUTHOR]

[Display omitted] • AGDE is utilized to optimize quantum cloning circuit parameters. • The experimental results reveal that AGDE is outperformed the other well-known metaheuristics algorithms. • AGDE is minimized the parameter values of cloning difference error value down to 10−8. • The qualitative and quantitative measurements proved the superiority of AGDE. Quantum cloning operation, started with no-go theorem which proved that there is no capability to perform a cloning operation on an unknown quantum state, however, a number of trials proved that we can make approximate quantum state cloning that is still with some errors. To the best of our knowledge, this paper is the first of its kind to attempt using meta-heuristic algorithm such as Adaptive Guided Differential Evolution (AGDE), to tackle the problem of quantum cloning circuit parameters to enhance the cloning fidelity. To investigate the effectiveness of the AGDE, the extensive experiments have demonstrated that the AGDE can achieve outstanding performance compared to other well-known meta-heuristics including; Enhanced LSHADE-SPACMA Algorithm (ELSHADE-SPACMA), Enhanced Differential Evolution algorithm with novel control parameter adaptation (PaDE), Improved Multi-operator Differential Evolution Algorithm (IMODE), Parameters with adaptive learning mechanism (PALM), QUasi-Affine TRansformation Evolutionary algorithm (QUATRE), Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Cuckoo Search (CS), Bat-inspired Algorithm (BA), Grey Wolf Optimizer (GWO), and Whale Optimization Algorithm (WOA). In the present study, AGDE is applied to improve the fidelity of quantum cloning problem and the obtained parameter values minimize the cloning difference error value down to 10 - 8 . Accordingly, the qualitative and quantitative measurements including average, standard deviation, convergence curves of the competitive algorithms over 30 independent runs, proved the superiority of AGDE to enhance the cloning fidelity. [ABSTRACT FROM AUTHOR]

• New heuristic to solve the supplier selection and order quantity allocation problem. • Best solution compared to others reported in the literature for a known problem. • Better performance compared to Particle Swarm Optimization and Differential Evolution. This paper addresses the so-called supplier-selection and order-quantity allocation problem. Because of the complexity of this of problem (e.g., non-linear, discontinuous cost function), a new heuristic method is proposed and tested. This heuristic method explores the search space in a short period of time to find cheaper solutions. In order to test the efficiency of the proposed heuristic, two metaheuristic algorithms are applied: particle swarm optimization (PSO) and differential evolution (DE). Additionally, two numerical examples were solved. In the first one, it is shown that the proposed heuristic performed best compared to other solutions previously published in the literature, both in terms of computational time and total cost. The complexity analysis found that in the worst case, the proposed heuristic executes on average 99.9% less numerical operations than PSO and DE. In the second numerical example, larger instances were studied. Our findings show that the proposed heuristic was able to find a feasible solution in 15 out 15 instances, while the PSO and the DE algorithms were unable to find a solution in 9 out of 15 instances. Therefore, the proposed heuristic does not just lead to lower total cost solutions, but it also performs a more exhaustive search in shorter computational times for larger instances of the problem. Finally, Wilcoxon and Kruskal-Wallis statistical tests demonstrate significant difference between the proposed heuristic and the PSO and DE; the proposed heuristic presented a lower median in most cases. [ABSTRACT FROM AUTHOR]

• An improved algorithm NAS-GWO proposed for agricultural UAV trajectory planning. • Using nonlinear parameters to balance the algorithm's local and global search. • Combining Gaussian mutation and spiral function aids algorithms skip local minima. • Improve the position utilization of optimal individuals by boundary constraints. • NAS-GWO's trajectory planning was excellent in four different scale mission tests. Unmanned aerial vehicle (UAV) trajectory planning plays an essential role in agricultural production and biological control. To solve the agricultural UAV trajectory planning problem, a multi-mechanism collaborative improved grey wolf optimization algorithm (NAS-GWO) is proposed. In NAS-GWO, the evolutionary boundary constraint processing mechanism is introduced to update the position of the grey wolf individuals that cross the boundary in time to retain the position information of the optimal individuals to the largest degree to enhance the search accuracy of the algorithm. Then, the Gaussian mutation strategy and spiral function are used as perturbation mechanisms to help the algorithm jump out of the local optimum in time to strengthen the exploitation capability of NAS-GWO. Meanwhile, the improved Sigmoid function is used as a nonlinear convergence factor for balancing the exploitation and exploration of the NAS-GWO. By comparing NAS-GWO with ten advanced metaheuristic algorithms on 20 CEC2017 benchmark functions, the experimental results show that the NAS-GWO algorithm has superior merit seeking and robustness. Moreover, the agricultural UAV trajectory planning problem is solved using NAS-GWO. The experimental results show that the NAS-GWO algorithm plans a more viable and stable trajectory path in four different scale missions, while the most important is that it requires less cost. Among them, the algorithm reduces 27.93%, 38.15%, 32.32%, 34.11%, 10.63%, and 13.48% on average in cost function values compared to Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Aquila Optimizer (AO), Differential Evolution (DE), Dung Beetle Optimizer (DBO), and Grey Wolf Optimization algorithm (GWO), thus proving the effectiveness and significance of NAS-GWO in the agricultural UAV trajectory path planning problem. [ABSTRACT FROM AUTHOR]

Color Quantization (CQ) is a complex and hard problem because selecting the best set of colors from many available colors and using that set to obtain a good quality image is an NP-complete problem. The use of evolutionary computation and swarm-based methods to solve search and optimization problems has increased dramatically in recent years. This article compares some of these methods in order to solve the CQ problem. The following methods were used to generate CQ images: Particle swarm optimization, Artificial bee colony, Adaptive differential evolution, Success-history based adaptive differential evolution (with and without linear population size reduction), Cuckoo search, Firefly algorithm and Shuffled-frog leaping algorithm. For the first two methods, two variants were considered. Thus, a total of ten metaheuristics were compared with four classical CQ methods (Variance-based, Median-cut, Binary splitting and Wu's methods) applying them to a set of benchmark images and considering four different palette sizes (32, 64, 128, and 256 colors). Three error measures were considered to compare the methods: the mean squared error, the mean absolute error and the peak signal-to-noise ratio. Some of the swarm-based methods analyzed include a recently proposed CQ method using ants. Although they have a slow computational speed in the experimental studies, the ant-based methods are significantly better than all other methods according to the Wilcoxon signed rank test. In general, despite their speed, classical methods underperform the other ten methods both qualitatively and quantitatively. • Population-based methods generate better images than classical methods. • Ant-tree heuristic based color quantization yield high quality images. • Particle swarm optimization plus artificial ants outperforms the other methods. • Some good differential evolution variants do not outperform swarm-based methods. • Compared differential evolution-based methods generate similar quality images. [ABSTRACT FROM AUTHOR]

Biopsy tests used in the identification and confirmation of breast cancer are time-consuming and complex. Thus, neural networks can be applied to aid specialists with a prone to be in local minima, which can be avoided using evolutionary algorithms. In that way, this work analyzed the methods of genetic algorithm, differential evolution, differential evolution with islands and migration, particle swarm optimization, and adaptive particle swarm optimization in the training of neural networks for the classification of histological images stained by hematoxylin-eosin. The differential evolution with islands and migration and particle swarm optimization presented the most promising results, with the first one reaching a maximum AUC of 0.70 for the color features, and the last one with a maximum AUC of 0.71 for the texture attributes, on the evaluated dataset. Through this proposal, we have an important contribution to breast cancer histological image classification that also allows the development of new studies in the future. • Definition of diagnosis of breast cancer by images is timeconsuming and complex. • Artificial neural networks can be used to aid specialists in this task. • Evolutionary algorithms were not yet explored in the literature for that. • We analyzed the use of these techniques for classifying breast cancer images. • The performed experiments demonstrate the contribution of the proposed method. [ABSTRACT FROM AUTHOR]

In this study, an energy efficiency study has been carried out through the moment values of a quadrotor applied in the position control with the differential evolution algorithm (DE), which is under the evolutionary algorithms among meta-heuristic algorithms particle swarm optimization (PSO) which is among swarm-based algorithms and gravity search algorithm (GSA) among physics-based algorithms and charged system search (CSS) algorithm, which are all well-known in the literature. The efficient use of energy in electrically powered flying mechanisms is important in terms of completing its task. The aim of this study is to obtain proportional–integral–derivative (PID) coefficients that minimize the energy consumption caused by quadrotor moments. First of all, 12 PID coefficients calculated with traditional Ziegler Nicholes, and trying-error methods for the control of the quadrotor have been trained with DE, PSO, GSA and CSS algorithms and their cost values have been found for this problem. In the study, optimum PID coefficients have been found separately with each algorithm and torque data giving energy efficiency have been obtained by applying them to the state equations. Although the cost minimization convergence value obtained from DE does not look good compared to other algorithms, it has been observed that the moment values of the obtained PID coefficients in the control program are lower. In the study, it has been shown that the PID coefficients obtained from DE reach the target with approximately less energy than the maximum-moment value of the PSO, which provides the closest moment value. It can be thought that this value contributes to energy consumption in each orbital movement of the quadrotor. The simulation results show that the coefficients obtained from DE provide better energy efficiency and are superior to other algorithms compared in terms of energy efficiency. • PID coefficients for quadrotor control are optimized with DE,PSO,CSS,GSA algorithms • The torque values to be applied to each motor were adjusted with these coefficients • The total force applied with DEPID in the z direction is smaller than others • DEPID consumes less energy than the other algorithms in the paper [ABSTRACT FROM AUTHOR]

In this study, an energy efficiency study has been carried out through the moment values of a quadrotor applied in the position control with the differential evolution algorithm (DE), which is under the evolutionary algorithms among meta-heuristic algorithms particle swarm optimization (PSO) which is among swarm-based algorithms and gravity search algorithm (GSA) among physics-based algorithms and charged system search (CSS) algorithm, which are all well-known in the literature. The efficient use of energy in electrically powered flying mechanisms is important in terms of completing its task. The aim of this study is to obtain proportional–integral–derivative (PID) coefficients that minimize the energy consumption caused by quadrotor moments. First of all, 12 PID coefficients calculated with traditional Ziegler Nicholes, and trying-error methods for the control of the quadrotor have been trained with DE, PSO, GSA and CSS algorithms and their cost values have been found for this problem. In the study, optimum PID coefficients have been found separately with each algorithm and torque data giving energy efficiency have been obtained by applying them to the state equations. Although the cost minimization convergence value obtained from DE does not look good compared to other algorithms, it has been observed that the moment values of the obtained PID coefficients in the control program are lower. In the study, it has been shown that the PID coefficients obtained from DE reach the target with approximately less energy than the maximum-moment value of the PSO, which provides the closest moment value. It can be thought that this value contributes to energy consumption in each orbital movement of the quadrotor. The simulation results show that the coefficients obtained from DE provide better energy efficiency and are superior to other algorithms compared in terms of energy efficiency. • PID coefficients for quadrotor control are optimized with DE,PSO,CSS,GSA algorithms • The torque values to be applied to each motor were adjusted with these coefficients • The total force applied with DEPID in the z direction is smaller than others • DEPID consumes less energy than the other algorithms in the paper [ABSTRACT FROM AUTHOR]

• A method of period partitioning based on the silhouette coefficient is proposed. • A TOU price optimization model considering benefits and reliability is established. • A multi-objective simulated annealing particle swarm optimization algorithm is developed. • A bi-level TOU price optimization model for high reliability demand system is presented. The reliability of power system will vary with different demand response strategies, and it is significant to make a decision on demand response for satisfying differential reliability demand of power system. First, a peak-valley period partition model with an objective of maximizing silhouette coefficient was established based on the fuzzy clustering and step-wise iteration technology. Second, this paper proposed a multi-objective time of use electricity price optimization model considering the interests of both supply and demand sides, as well as reliability demand, and a multi-objective simulated annealing particle swarm optimization algorithm was developed to obtain solution. Third, the Pareto front curve of the dual-objective function was fitted by using a third-order Hermite interpolation algorithm, and the time of use prices corresponding to the reliability demand was obtained by using the back propagation neural network algorithm. Finally, the proposed model and algorithm were verified by the RBTS system, and the results indicate good rationality and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Due to escalating permeation of renewable energy sources, it becomes essential to investigate its brunt on the optimal power generation scheduling. But, highly intermittent nature of renewable energy sources and their higher rate of outages may have detrimental effect on the entire grid. This work recommends chaotic fast convergence evolutionary programming (CFCEP) rooted in Tent equation to solve hydrothermal generation scheduling incorporating pumped-storage-hydraulic (PSH) unit with demand side management (DSM) considering uncertainty and outage of renewable energy sources. Chaotic sequences increase the exploitation ability in the searching space and enhance the convergence property. In the recommended technique, chaotic sequences have been pertained for acquiring the dynamic scaling factor setting in fast convergence evolutionary programming (FCEP). Simulation outcomes of the test system have been matched up to those acquired by FCEP, differential evolution (DE) and particle swarm optimization (PSO). It has been observed from the comparison that the recommended CFCEP technique has the capability to bestow with superior-quality solution. • CFCEP is used for solving hydro-thermal generation scheduling with DSM. • Pumped-storage-hydraulic unit and renewable energy sources are considered. • Uncertainty and outage of renewable energy sources are also taken into consideration. • Simulation outcomes have been matched up to those acquired by FCEP, DE and PSO. [ABSTRACT FROM AUTHOR]

• A two-archive-guided multiobjective artificial bee colony algorithm was designed. • The algorithm's convergence and exploitation abilities are enhanced. • Two archives are employed to enhance the search capability of the algorithm. • Results have shown that TMABC-FS is an efficient and robust optimization method. Since different features may require different costs, the cost-sensitive feature selection problem become more and more important in real-world applications. Generally, it includes two main conflicting objectives, i.e. , maximizing the classification performance and minimizing the feature cost. However, most existing approaches treat this task as a single-objective optimization problem. To satisfy various requirements of decision-makers, this paper studies a multi-objective feature selection approach, called two-archive multi-objective artificial bee colony algorithm (TMABC-FS). Two new operators, i.e. , convergence-guiding search for employed bees and diversity-guiding search for onlooker bees, are proposed for obtaining a group of non-dominated feature subsets with good distribution and convergence. And two archives, i.e. , the leader archive and the external archive are employed to enhance the search capability of different kinds of bees. The proposed TMABC-FS is validated on several datasets from UCI, and is compared with two traditional algorithms and three multi-objective methods. Results have shown that TMABC-FS is an efficient and robust optimization method for solving cost-sensitive feature selection problems. [ABSTRACT FROM AUTHOR]

This paper suggests chaotic fast convergence evolutionary programming (CFCEP) for solving multi-region dynamic economic dispatch (MRDED) problem, multi-region economic dispatch (MRED) problem and economic dispatch (ED) problem. MRDED problem is based on multi-reservoir cascaded hydro plant with time delay, thermal plants with nonsmooth fuel cost function, wind and solar power units with uncertainty and pumped hydro energy storage. MRED problem deals with tie line constraints, transmission losses, valve point effect and proscribed workable area of thermal generators. ED problem deals with valve point effect, proscribed workable area and ramp rate limits of thermal generators. In the recommended technique, chaotic sequences have been pertained for acquiring the dynamic scaling factor setting in fast convergence evolutionary programming (FCEP). The efficacy of the proposed technique has been verified on convoluted three-area system for MRDED problem, four-area system for MRED problem and 140-unit Korean system for ED problem. Test results acquired from the suggested CFCEP technique have been fit to that acquired from FCEP, differential evolution (DE) and particle swarm optimization (PSO). It has been observed from the comparison that the recommended CFCEP technique has the capability to bestow with better-quality solution. • This work recommends CFCEP for solving MRDED, MRED and ED problem. • The solar–wind–hydro–thermal power system is taken into consideration. • Wind and solar power uncertainty and pumped hydro energy storage are considered. • Simulation outcomes of the test system are matched up by FCEP, DE and PSO. [ABSTRACT FROM AUTHOR]

• A two-layer hierarchical differential evolution (DE) algorithm is proposed. • Success-history-based adaptive DE (SHADE) is implemented in the bottom layer. • Multi-cross operation (MCO) is implemented in the top layer. • The first-order stability is analyzed without a stagnation assumption. • The proposed DE yields more accurate solutions for 30- and 50-dimensional problems. In expert systems, complex optimization problems are always characterized by nonlinearity, nonconvexity, multi-modality, discontinuity, and high dimensionality. Although classical optimization algorithms are mature, they readily fall into a local optimum. The differential evolution (DE) algorithm has been successfully applied to solve numerous problems with expert systems. However, balancing the global and local search capabilities of the DE algorithm remains an open issue and has attracted significant research attention. Thus, a hierarchical heterogeneous DE algorithm that incorporates multi-cross operation (MCO) is proposed in this article. In the proposed algorithm, success-history-based adaptive DE (SHADE) is implemented in the bottom layer, while MCO is implemented in the top layer. The MCO search is based on the SHADE results, but its search results do not affect the bottom layer. First-order stability analyses conducted for the presented MCO showed that the individual positions are expected to converge at a fixed point in the search space. The accuracy and convergence speed of the proposed algorithm were also experimentally compared with those of eight other advanced particle swarm optimization techniques and DE variants using benchmark functions from CEC2017. The proposed algorithm yielded better solution accuracy for 30- and 50-dimensional problems than the other variants, and although it did not provide the fastest convergence for all of the functions, it ranked among the top three for the unimodal and simple multimodal functions and achieved fast convergence for the other functions. [ABSTRACT FROM AUTHOR]

Biologically inspired optimization algorithms (BIOA), such as particle swarm optimization (PSO) and differential evolution (DE), are often applied to a specific problem with the tacit assumptions that each algorithm will have a unique set of "ideal" tuning parameters that provide "best" performance, and that, once tuned, two different optimization algorithms ought to exhibit distinct performance. This work tests those assumptions by systematically comparing PSO and DE applied to identify stable and metastable configurations of a benchmark system, a titania dimer Ti 2 O 4 , whose energy is described by a modified Matsui-Akaogi pairwise potential. The hypotheses (H) are formally expressed as: (H1) for both PSO and DE there exists a unique, optimal set of tuning parameters that maximize its performance on a given problem; (H2) when running two different BIOAs, one algorithm must demonstrate superior performance over the other. By identifying and studying an approximate Pareto-optimal set of tuning parameters for DE and PSO, H1 is shown to be strictly false for DE, and tentatively false for PSO, while H2 is shown to be false with respect to certain statistics. We further investigate the correlations between key tuning parameters and algorithm performance. Our conclusion may be implemented to further optimize and develop BIOAs applied to materials systems. [ABSTRACT FROM AUTHOR]

Success-History-Based Parameter Adaptation Multi-Objective Differential Evolution (SHAMODE) has shown excellent performance recently compared to other multi-objective evolutionary algorithms (MOEAs) and therefore caught the interest of researchers lately. The SHAMODE framework employs historical memory of control parameters, which stores set values of control parameters that have performed well in the past generation and produces new set values of the parameter by directly sampling the parameter space close to one of these stored pairs. Since the algorithm's performance depends on the problem to be optimized, this paper proposes an enhanced version of SHAMODE that is compatible with optimizing the multi-objective wave energy converter problem. The improved version of SHAMODE is achieved by incorporating competing machine learning algorithms, Particle Swarm Optimization, and a modified bubble-net attacking method formula of the Whale optimization algorithm from its original form. Simulations and comparisons are used to verify the proposed method's performance based on some famous multi-objective problem (MOP) benchmark functions. The results reveal that the proposed algorithm performs better than other algorithms that have been recently developed. In addition, the suggested technique is used to optimize the multi-objective wave energy converter model in order to maximize the annual production of electric energy while minimizing the per-unit energy cost. • Machine Learning and PSO based Success-History Based Parameter Adaptation Multi-Objective Differential Evolution algorithm was proposed. • The Improved version of SHAMODE optimized the WEC system to maximize the annual power generated at a minimum Levelized cost of energy. • The Improved version of SHAMODE was applied to optimize the multi-objective wave energy converter model to maximize the annual generated power and minimize the per unit energy cost. [ABSTRACT FROM AUTHOR]

In joint replenishment problem with random number of imperfect items (IJRP), it is a great challenge for managers to correctly ascertain the number of imperfect items and take the right response strategy to make rectifications simultaneously. In this paper, the effectiveness of four common response strategies, i.e., scrap, all-unit quantity discount, rework & repair, and buy replacements for coping with imperfect items are investigated based on IJRP for the first time, respectively. Considering the NP -hard nature of the classical JRP and its extensions, as well as enhancing the explorable performance in crossing scale-levels of IJRP models and exploitable performance in diversifying the solution population of bare-bones differential evolutionary (BBDE), a meta-heuristic algorithm named as the adaptive BBDE (ABBDE) is designed by introducing an problem scale related adaptive recombination probability and a generalized opposition-based learning (OBL). Through numerical experiments, it has been found that (1) The adaptive improvements of ABBDE have been verified through obtaining the lower best-found total cost (TC), the lowest mean value, and the smallest standard error comparing to that of genetic evolution algorithm (GA), particle swarm optimization (PSO), differential evolution (DE), bare-bones PSO (BBPSO), BBDE. (2) Parameter sensitivity analyses indicate that the scale of yearly demand, lead-time, and significance of uncertainty have significant impacts on system cost, whilst the parameters pertinent to specific response strategy are main factors causing system cost disturbance. (3) Management insights and limitations on adoption of a right response strategy and the practical usage of ABBDE are obtained. • Four response strategy influences for coping with defective items are examined. • Both random imperfect items and stochastic demands are considered. • An adaptive mutation probability and opposition-based learning are considered. • Numerical experiments are designed and extensively performed. • Insights are obtained through experiments on different scale cases. [ABSTRACT FROM AUTHOR]

• Hybrid modelling showed higher accuracy than the first-principle model. • Polynomial statistical functions perform better than MRF function. • Differential evolution search is more accurate than the Particle Swarm Optimisation. • The training strategy has a significant impact on the models accuracy. • The complexity of the cost function showed a crucial role in the model training. Applying machine learning (ML) techniques is a complex task when the data quality is poor. Integrating first-principle models and ML techniques, namely hybrid modelling significantly supports this task. This paper introduces a novel approach to developing a hybrid model for dynamic chemical systems. The case in analysis employs one first-principle structure and two ML-based predictors. Two training approaches (serial and parallel), two optimisers (particle swarm optimisation and differential evolution) and two ML functions (multivariate rational function and polynomial) are tested. The polynomial function trained with the differential evolution showed the most accurate and robust results. The training approach does not significantly affect the hybrid model accuracy. However, the main effect of the training approach is on the robustness of the parameter predictions. The coefficients of determination (R2) on the test batches are above 0.95. In addition, it showed satisfactory extrapolation capabilities on different production scales with R2>0.9. [ABSTRACT FROM AUTHOR]

Implicit reservoir operation rules map known reservoir conditions to discharge flow or water level decisions to guide reservoir operation. Existing data-driven operation rule extraction models are influenced by both features and hyperparameters, and lack subjective-objective coordination, hindering the formulation of reasonable water energy utilization strategies. To improve the comprehensive benefits of cascade reservoirs, this study proposes a novel multi-objective cascade reservoir operation rule derivation framework. First, a multi-objective optimal operation model for cascade reservoirs (MOOMCR) considering power generation, environment, and navigation is constructed and solved using a Speed-constrained Multi-objective Particle Swarm Optimization algorithm (SMPSO). The relationships among the requirements were explained using a four-dimensional Pareto front visualization analysis. Then, an Integrated Decision-making Method based on Multilevel Subspace Domination (IDMSD) is proposed to circumvent the subjective defects of single decision-making models and reliably select the optimal scheme with the most significant comprehensive benefits from all typical years. Finally, a feature-hyperparameter combination optimization technique based on the differential evolutionary algorithm (FHCC- DE / best /1) is proposed, which exhibits its strength in the identification of key influencing factors on the optimal discharge flow from the IDMSD. The method was embedded in a deep learning model to construct a Combinatorial Evolution Network (CEN) to extract the implicit reservoir operation rules from the optimal IDMSD schemes for the MOOMCR. The results show that the RMSE of the CEN simulating the optimal discharge flow is improved by 5.98%, 28.03%, and 27.89% for the XLD reservoir and by 11.01%, 1.66%, and 60.98% for the XJB reservoir, respectively, over the best results of all benchmark models. Meanwhile, the operation simulation results confirmed that the CEN outperforms conventional reservoir scheduling, with advanced multi-objective comprehensive effects. This suggests that multimethod integrated decision-making and deep learning evolution optimization can help improve the accuracy, generalization, and reliability of implicit reservoir operation rules. [Display omitted] • An integrated decision method is proposed to balance reservoir operation demands. • A deep learning evolution optimization model is proposed to derive operation rules. • Feature and hyperparameter is optimized simultaneously in rules derivation models. • The method framework improves the comprehensive benefits for cascade reservoirs. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid machine learning method for the prediction of concrete expansion induced by alkali-silica reaction (ASR) and assembles a comprehensive and reliable experimental database comprising of around 1900 sets of ASR expansion data from literature to calibrate and validate the machine learning-based prediction model. The hybrid machine learning method employs a beta differential evolution-improve particle swarm optimization algorithm (BDE-IPSO) to tune weights and biases of the artificial neural network (ANN) model. The model adopts 11 variables as input, in terms of material composition, specimen geometry and environmental conditions, and can predict ASR expansion with great accuracy. The results demonstrate that the established prediction model is able to capture all available experimental aspects of ASR expansion, including: (a) effects of reactivity, size, content of reactive aggregate, water-to-cement ratio, and alkali concentration; (b) effects of temperature and relative humidity; (c) size effects of specimen geometry; and (d) the time-dependent behavior. • A comprehensive database consisting of 1896 sets of ASR expansion data is gathered. • A hybrid machine learning method for predicting concrete expansion induced by ASR is proposed. • The proposed hybrid machine learning method can predict ASR expansion with great accuracy. • ASR expansion is predicted based on material composition, specimen geometry and environmental conditions. • The prediction model is able to capture all available experimental evidence of ASR expansion. [ABSTRACT FROM AUTHOR]

Highlights • Monthly precipitation was predicted using soft computing methods including ANFIS with GA, ACOR, PSO and DE. • Suggested algorithms improved the ANFIS performance for modeling the precipitation. • Suggested methods decreased the event of trapping in local optima. • ANFIS-ACOR model with least error and most correlation showed highest performance in monthly precipitation forecast. Abstract Existent algorithms to train adaptive neuro-fuzzy inference system (ANFIS) such as gradient descent and least-square methods are useful, but they have problems such as trapping in local optimum as well as high-volume of computations. This paper examines the application of genetic algorithm (GA), ant colony optimization for the continuous domain (ACO R), particle swarm optimization (PSO) and differential evolution (DE) for improving the performance of ANFIS models in simulating monthly rainfall magnitudes. Monthly rainfall records of nine foothills to arid weather stations of Isfahan province, central Iran were used for evaluating the proposed methodology. The correlation coefficient and non-linear sensitivity analysis were performed on the available rainfall records. Monthly rainfall modelling was carried out based on three input groups and used separately in five applied models (e.g. ANFIS, ANFIS-PSO, ANFIS-ACO R , ANFIS-DE, and ANFIS-GA). The obtained results showed that proposed hybrid models had better accuracy than the simple ANFIS model in escaping local optima. The best model was ANFIS-ACO R (with R2 = 0.92, RMSE = 2.73 mm and SI = 0.26), while sole ANFIS model (with R2 = 0.33, RMSE = 9.98 mm and SI = 0.90) presented the worst output. Moreover, the obtained results showed that: (1) Coupling the optimization algorithms with ANFIS can improve its performance in modeling the monthly rainfall time series; (2) the correlation coefficient and sensitivity analysis of input data provided a valid input selection that could improve the model accuracy with neglecting the redundant input parameters. [ABSTRACT FROM AUTHOR]

Abstract Fast and efficient determination of the mechanical parameters of surrounding rock masses is vitally important to the calculation and evaluation of the stability of surrounding rock masses in tunnel engineering. In this paper, a displacement back-analysis (DBA) model is proposed to identify the mechanical parameters based on support vector regression (SVR) optimized by multi-strategy artificial fish swarm algorithm (MAFSA). The MAFSA adopts the differential evolution strategy, the particle swarm optimization strategy, the adaptive step size and phased vision strategy on the basis of artificial fish swarm algorithm (AFSA) to enhance the global search capability and improve convergence speed and optimization accuracy. Then, the kernel width and the penalty parameter of SVR are optimized by MAFSA, forming into MAFSA-SVR. Meanwhile, the training and testing samples for MAFSA-SVR are constructed by orthogonal design and forward calculation by FLAC3D code. Finally, the DBA model is established based on MAFSA-SVR and applied to the mechanical parameter inversion of surrounding rock masses in the Heshi tunnel with the following conclusion: the relative errors of all the mechanical parameters are less than 8% between the inversed values of the DBA model based on MAFSA-SVR and the actual values. The method proposed in this paper could provide an efficient tool for the mechanical parameter inversion of the tunnel surrounding rock masses. [ABSTRACT FROM AUTHOR]

Abandoned areas with substances potentially harmful to the environment or human health have raised numerous concerns around the world. The objective of the present study is to analyze the possibility of building solar power plants capable of capturing solar energy in unproductive areas, both contaminated and uncontaminated. For this purpose, American data from the National Solar Radiation Database (NSRDB) were used, a collection of hourly solar radiation measurements and meteorological data, as well as data from the RE-Powering America's Land project, run by the United States Environmental Protection Agency (EPA). In the analysis, the information about “mapped area”, “distances to the transmission lines”, “solar direct normal irradiance on a utility scale” and “off-grid direct normal irradiance” were considered. To define the best locations, the data were initially pre-processed. A new hybrid fuzzy c-means (HFCM) algorithm was then applied, initialized, comparatively, by three metaheuristics: Differential Evolution (DE), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), for the data clustering. The number of clusters obtained was validated by three metrics: Calinski-Harabasz Criterion, Davies-Bouldin Criterion and Silhouette Coefficient, with all three unanimously indicating two clusters as the ideal number: one cluster for locations with greater potential for allocating facilities to capture solar energy, and another for locations with a lower potential. With the new approach, an increase of 23.3% in the training velocity of the HFCM algorithm was identified, which required fewer iterations to achieve the same value of the objective function. Else, a round of experiments was conducted with six different datasets (instances from literature) and the results showed that the proposed method can achieve better results (faster convergence and smaller solution cost) than the classic FCM. Visually, the predominance of the allocation of facilities was perceived in states with a greater average incidence of solar radiation. Therefore, this was the predominant factor in the convergence of the algorithm, which is in accordance with expectations for solar energy. Finally, the social, economic and environmental gains were considered with the revitalization of unproductive land with the possibility of implementing solar power plants in these areas. [ABSTRACT FROM AUTHOR]

This paper proposes an effectively adaptive selective cuckoo search algorithm (ASCSA) for solving short-term hydrothermal scheduling problems with available water constraint, reservoir volume constraints, and transmission network constraints. The proposed ASCSA is a newly improved version of the conventional cuckoo search algorithm to enhance the solution quality and reduce the maximum number of iterations based on two new techniques including the new ratio of the difference between the fitness function values and the integration of solutions into one group. The effectiveness of ASCSA has been validated via eight hydrothermal systems, in which the last two systems consisting of the IEEE 30-bus and IEEE 118-bus systems are considered with a set of constraints in the transmission network. To investigate the performance of ASCSA, several algorithms are also implemented in the paper such as conventional cuckoo search algorithm, modified cuckoo search algorithm, particle swarm optimization, global vision of particle swarm optimization with inertia weight, differential evolution, and improved differential evolution. From result comparisons of the test systems, the proposed ASCSA method has obtained lower total costs than other methods implemented for solving the problems. Therefore, the proposed ASCSA is a very efficient and favorable method for solving the considered hydrothermal scheduling problems. [ABSTRACT FROM AUTHOR]

This work examines a strategy for combining two heuristic methods, Differential Evolution (DE) and Particle Swarm Optimization (PSO) to better address the problem of stagnation when solving time-optimal slew-maneuver problems with path constraints. Several approaches are considered, including the use of only PSO or DE, and use of PSO with DE applied to some iterations. Applying a newer form of DE, in which random fractions of the vector-difference components are employed, stagnation is not evident, but the computational time is greater. The combined PSO/DE method handles the stagnation very well, with a 40% reduction in computation time compared with just DE. [ABSTRACT FROM AUTHOR]

An evolutionary computation framework to learn binary threshold networks is presented. Inspired by the recent trend of binary neural networks, where weights and activation thresholds are represented using 1 and -1 such that they can be stored in 1-bit instead of full precision, we explore this approach for gene regulatory network modeling. We test our method by inferring binary threshold networks of two regulatory network models: Quorum sensing systems in bacterium Paraburkholderia phytofirmans PsJN and the fission yeast cell-cycle. We considered differential evolution and particle swarm optimization for the simulations. Results for weights having only 1 and -1 values, and different activation thresholds are presented. Full binary threshold networks were found with minimum error (2 bits), whereas when the binary restriction is relaxed for the activation thresholds, networks with 0 bit error were found. [ABSTRACT FROM AUTHOR]

[Display omitted] • Economic benefit, load period, load power supply reliability by capacity utilization maximum of energy storage are explored for Wind/photovoltaic generation. • A multi-objective with multiple constraint factors scheduling model is proposed on HESS power throughput. • A new energy storage dispatching optimization algorithm is proposed based on MSIIO (multi-sequence index iterative optimization). • Feasibility is verified by Matlab tool through joint application of utility grid -connected controller and virtual simulation system. The asynchrony between wind/photovoltaic complementary power supply and load demand change will affect the stable operation of the microgrid. Considering the stability and economic benefits of micro grid, a multi-objective dispatching model with multi factor constraints is proposed. Firstly, considering the effective utilization of energy storage capacity, the dispatching model is solved based on multiple series exponential iterative optimization (MSIIO) with the micro-objectives of maximum revenue and load supply reliability. Secondly, the logic algorithm of utility grid connection controller is studied according to different operation scenarios in combination with peak load regulation and valley filling dispatching strategy, so that the system can work in island mode or utility grid connection mode. Finally, a virtual simulation system is used to verify the feasibility of the dispatching strategy. Comparing with particle swarm optimization and differential evolution algorithm, the results of MSIIO show that the economic benefit is increased by 4.18%, the capacity utilization rate reached 82.83%. [ABSTRACT FROM AUTHOR]

In the mid 1990s two landmark metaheuristics have been proposed: Particle Swarm Optimization and Differential Evolution. Their initial versions were very simple, but rapidly attracted wide attention. During the last quarter century hundreds of variants of both optimization algorithms have been proposed and applied in almost any field of science or engineering. However, no broader comparison of performance between both families of methods has been presented so far. In the present paper ten Particle Swarm Optimization and ten Differential Evolution variants, from historical ones from the 1990s up to the most recent ones from 2022, are compared on numerous single-objective numerical benchmarks and 22 real-world problems. On average Differential Evolution algorithms clearly outperform Particle Swarm Optimization ones. Such advantage of Differential Evolution over Particle Swarm Optimization is in contradiction with popularity: In the literature Particle Swarm Optimization algorithms are two–three times more frequently used than Differential Evolution ones. Problems for which Particle Swarm Optimization performs better than Differential Evolution do exist but are relatively few. Although this result may be an effect of the choice of specific variants, experimental settings or problems used for comparison, some re-consideration of algorithmic philosophy may be needed for Particle Swarm Optimization variants to make them more competitive. [ABSTRACT FROM AUTHOR]

Particle swarm optimization (PSO) algorithm has a potential to solve route planning problem for unmanned aerial vehicle (UAV). However, the traditional PSO algorithm is easy to fall into local optimum under the complicated environments with multiple threats. In order to improve the performance in different complicated environments, a novel and effective PSO algorithm with adaptive adjustment of the parameters, cylinder vector and different evolution operator, named ACVDEPSO, is proposed and demonstrated to be effective for route planning problem for UAV. In the proposed ACVDEPSO, the velocity of the particle is converted to its cylinder vector for the convenience of the path search. It is worth highlighting that the parameters of ACVDEPSO algorithm are automatically chosen by the time and the fitness values of the particles. Furthermore, a challenger based on differential evolution operator is introduced to reduce the probability of falling into local optimum and accelerate the algorithm convergence speed. The simulation experiments have been conducted in real digital elevation model (DEM) maps to test the performance of the ACVDEPSO. The experiment results validate that the optimization performance of the ACVDEPSO outperforms the other comparison methods, which can efficiently generate a higher quality path for UAV under the complicated 3D environments. • A novel cylinder vector-based PSO algorithm with wave and Sigmoid functions is proposed. • An efficient path planning method based on cylinder vector-based PSO is proposed. • A new adaptive strategy of the inertia weight based on wave function is developed. • A new control scheme of acceleration coefficients based on sigmoid function is designed. [ABSTRACT FROM AUTHOR]

An optimal sizing model for a hybrid micro-grid system (HMGS) consisting of solar photovoltaic, wind turbine, diesel generator and battery is developed. A tri-objective formulation, considering techno-socio-economic factors, is proposed for finding the optimal size and configuration. Dynamic domain search is employed using a hybrid intelligent computational technique that integrated the improved particle swarm optimization (PSO) algorithm with an enhanced differential evolution (DE) model for improving the search efficiency. The idea behind the hybridization of these two popular paradigms is to deploy the higher exploration capability of the PSO during the initial search and then employ the fast convergence property of the DE to achieve an optimal solution through exploitation. A fuzzy attainment module (FAM) is appended to identify the best solution based on the highest possible attainment level for all objectives. Two power management strategies are modelled and their effect on emission curtailment is studied. Optimal sizing is analysed considering the effect of selected optimization objective(s) and multiple choices are offered to the designer based on preferences. For the selected remote island, the system with solar PV capacity 78.44 kW, wind turbine of 95 kW with a battery of 2 kW is found to be the best option. The levelized cost of energy (LCOE) is 0.0799 $/kWh, human development index (HDI) is 0.6454, unmet power is zero and the satisfaction level of all the three objectives is 72.82%. • Optimal sizing of hybrid energy system is proposed using an intelligent method. • Multi-objective problem is formulated with technical-economic and social factors. • Two battery power management strategies are employed. • Improved hybrid PSO-DE algorithm is employed for optimization. • Fuzzy attainment module is integrated to get the best attainment solution. [ABSTRACT FROM AUTHOR]

A number of physical or data-driven models have been proposed to evaluate stream water temperatures based on hydrological and meteorological observations. However, physical models require a large amount of information that is frequently unavailable, while data-based models ignore the physical processes. Recently the air2stream model has been proposed as an intermediate alternative that is based on physical heat budget processes, but it is so simplified that the model may be applied like data-driven ones. However, the price for simplicity is the need to calibrate eight parameters that, although have some physical meaning, cannot be measured or evaluated a priori . As a result, applicability and performance of the air2stream model for a particular stream relies on the efficiency of the calibration method. The original air2stream model uses an inefficient 20-year old approach called Particle Swarm Optimization with inertia weight. This study aims at finding an effective and robust calibration method for the air2stream model. Twelve different optimization algorithms are examined on six different streams from northern USA (states of Washington, Oregon and New York), Poland and Switzerland, located in both high mountains, hilly and lowland areas. It is found that the performance of the air2stream model depends significantly on the calibration method. Two algorithms lead to the best results for each considered stream. The air2stream model, calibrated with the chosen optimization methods, performs favorably against classical streamwater temperature models. The MATLAB code of the air2stream model and the chosen calibration procedure (CoBiDE) are available as Supplementary Material on the Journal of Hydrology web page. [ABSTRACT FROM AUTHOR]

The storage ring lattice optimization is a class of constrained multi-objective optimization problem, where in addition to low beam emittance, a large dynamic aperture for good injection efficiency and improved beam lifetime are also desirable. The convergence and computation times are of great concern for the optimization algorithms, as various objectives are to be optimized and a number of accelerator parameters to be varied over a large span with several constraints. In this paper, a study of storage ring lattice optimization using differential evolution is presented. The optimization results are compared with two most widely used optimization techniques in accelerators—genetic algorithm and particle swarm optimization. It is found that the differential evolution produces a better Pareto optimal front in reasonable computation time between two conflicting objectives-beam emittance and dispersion function in the straight section. The differential evolution was used, extensively, for the optimization of linear and nonlinear lattices of Indus-2 for exploring various operational modes within the magnet power supply capabilities. [ABSTRACT FROM AUTHOR]

This paper studies the optimization of wind turbine positioning with multiple hub heights on flat terrain using the Gaussian Particle Swarm Optimization (GPSO) with differential evolution. The hub height of each turbine within the wind farm is added to the optimization variables, and the horizontal coordinates and the hub heights of all the turbines are optimized simultaneously. The objective is to minimize the ratio of cost and power product. Three typical wind cases are employed to test the effectiveness of the present method. Numerical results reveal the necessity of the three-dimensional (3D) optimization. By comparing the optimized solutions with the ones by the greedy algorithm and the genetic algorithm, it is concluded that the present method is able to produce optimized solutions with lower cost per product and higher power output in most circumstances, especially in complicated situations. [ABSTRACT FROM AUTHOR]