Search

Various metaheuristic algorithms inspired by nature have been designed to deal with a variety of practical optimization problems. As an excellent metaheuristic algorithm, the improved particle swarm optimization algorithm based on grouping (IPSO) has strong global search capabilities. However, it lacks a strong local search ability and the ability to solve constrained discrete optimization problems. This paper focuses on improving these two aspects of the IPSO algorithm. Based on IPSO, we propose an improved particle swarm optimization algorithm based on variable neighborhood search (VN-IPSO) and design a 0-1 integer programming solution with constraints. In the experiment, the performance of the VN-IPSO algorithm is fully tested and analyzed using 23 classic benchmark functions (continuous optimization), 6 knapsack problems (discrete optimization), and 10 CEC2017 composite functions (complex functions). The results show that the VN-IPSO algorithm wins 18 first places in the classic benchmark function test set, including 6 first places in the solutions for seven unimodal test functions, indicating a good local search ability. In solving the six knapsack problems, it wins four first places, demonstrating the effectiveness of the 0-1 integer programming constraint solution and the excellent solution ability of VN-IPSO in discrete optimization problems. In the test of 10 composite functions, VN-IPSO wins first place four times and ranks the first in the comprehensive ranking, demonstrating its excellent solving ability for complex functions. [ABSTRACT FROM AUTHOR]

Reservoir flood control scheduling is a challenging optimization task, particularly due to the complexity of various constraints. This paper proposes an innovative algorithm design approach to address this challenge. Combining the basic walrus optimization algorithm with the adaptive ε-constraint method and introducing the SPM chaotic mapping for population initialization, spiral search strategy, and local enhancement search strategy based on Cauchy mutation and reverse learning significantly enhances the algorithm's optimization performance. On this basis, innovate an adaptive approach ε A New Algorithm for Constraints and Multi Strategy Optimization Improvement (ε-IWOA). To validate the performance of the ε-IWOA algorithm, 24 constrained optimization test functions are used to test its optimization capabilities and effectiveness in solving constrained optimization problems. Experimental results demonstrate that the ε-IWOA algorithm exhibits excellent optimization ability and stable performance. Taking the Taolinkou Reservoir, Daheiting Reservoir, and Panjiakou Reservoir in the middle and lower reaches of the Luanhe River Basin as a case study, this paper applies the ε-IWOA algorithm to practical reservoir scheduling problems by constructing a three-reservoir flood control scheduling system with Luanxian as the control point. A comparative analysis is conducted with the ε-WOA, ε-DE and ε-PSO (particle swarm optimization) algorithms.The experimental results indicate that ε-IWOA algorithm performs the best in optimization, with the occupied flood control capacity of the three reservoirs reaching 89.32%, 90.02%, and 80.95%, respectively. The control points in Luan County can reduce the peak by 49%.This provides a practical and effective solution method for reservoir optimization scheduling models. This study offers new ideas and solutions for flood control optimization scheduling of reservoir groups, contributing to the optimization and development of reservoir scheduling work. [ABSTRACT FROM AUTHOR]

BACKGROUND: The scientific revolution in the treatment of many illnesses has been significantly aided by stem cells. This paper presents an optimal control on a mathematical model of chemotherapy and stem cell therapy for cancer treatment. OBJECTIVE: To develop effective hybrid techniques that combine the optimal control theory (OCT) with the evolutionary algorithm and multi-objective swarm algorithm. The developed technique is aimed to reduce the number of cancerous cells while utilizing the minimum necessary chemotherapy medications and minimizing toxicity to protect patients' health. METHODS: Two hybrid techniques are proposed in this paper. Both techniques combined OCT with the evolutionary algorithm and multi-objective swarm algorithm which included MOEA/D, MOPSO, SPEA II and PESA II. This study evaluates the performance of two hybrid techniques in terms of reducing cancer cells and drug concentrations, as well as computational time consumption. RESULTS: In both techniques, MOEA/D emerges as the most effective algorithm due to its superior capability in minimizing tumour size and cancer drug concentration. CONCLUSION: This study highlights the importance of integrating OCT and evolutionary algorithms as a robust approach for optimizing cancer chemotherapy treatment. [ABSTRACT FROM AUTHOR]

Optimization methods are widely used in various fields to find the best possible solution to a given problem through the minimization or maximization of an objective function while adhering to specific constraints. In this paper, we present a new optimization algorithm, called WOADD, which was designed to handle the challenges of constrained optimization problems that involve dependent data. Unlike traditional algorithms that struggle with data dependencies and valid range constraints, WOADD uses a unique normalization process and a dynamic updating mechanism that accurately considers the interdependencies among features. By calculating a scaling parameter to navigate within feasible regions, WOADD adjusts its search strategy to ensure the preservation of data dependencies and adherence to constraints, ultimately leading to more efficient and precise optimization outcomes. Our extensive experimental analysis, which compared WOADD against other swarm optimization methods using a suite of benchmark functions, demonstrates its superior performance in terms of faster convergence rates, improved solution quality, and enhanced determinism in outcomes. [ABSTRACT FROM AUTHOR]

In the last few decades, the particle swarm optimization (PSO) algorithm has been demonstrated to be an effective approach for solving real-world optimization problems. To improve the effectiveness of the PSO algorithm in finding the global best solution for constrained optimization problems, we proposed an improved composite particle swarm optimization algorithm (ICPSO). Based on the optimization principles of the PSO algorithm, in the ICPSO algorithm, we constructed an evolutionary update mechanism for the personal best position population. This mechanism incorporated composite concepts, specifically the integration of the e-constraint, differential evolution (DE) strategy, and feasibility rule. This approach could effectively balance the objective function and constraints, and could improve the ability of local exploitation and global exploration. Experiments on the CEC2006 and CEC2017 benchmark functions and real-world constraint optimization problems from the CEC2020 dataset showed that the ICPSO algorithm could effectively solve complex constrained optimization problems. [ABSTRACT FROM AUTHOR]

The gait cycle of 25-degree of freedom (DOF) humanoid robot, namely NAO robot, consists of single support phase (SSP) and double support phase (DSP). Both dynamic and stability analyses are carried out for this robot to determine its power consumption and dynamic stability margin, respectively. Constrained single-objective optimization problems are formulated for the SSP and DSP separately and solved using particle swarm optimization (PSO) and genetic algorithms (GA). A performance index, other than the fitness function, consisting of constraint values and maximum swing height, is also considered to compare PSO and GA-obtained optimal solutions. PSO is able to find the trajectories that offer higher swing height for nearly similar power consumption during SSP. A performance assessment of each algorithm based on the best fitness values in each generation across several runs is also carried out. These values are compared using the Wilcoxon rank-sum test, and PSO is found to be statistically better than GA. The optimal solutions from the simulations are tested using the Webots simulator to validate their efficacy on stability. Moreover, an investigation of the influence of gait parameters on power consumption during SSP and DSP reveals that the humanoid robot with a higher hip height, lower swing height, and slow pace consumes less power. The methodology developed in this is generic and can be easily extended to other robots. [ABSTRACT FROM AUTHOR]

Drug discovery and development constitute a laborious and costly undertaking. The success of a drug hinges not only good efficacy but also acceptable absorption, distribution, metabolism, elimination, and toxicity (ADMET) properties. Overall, up to 50% of drug development failures have been contributed from undesirable ADMET profiles. As a multiple parameter objective, the optimization of the ADMET properties is extremely challenging owing to the vast chemical space and limited human expert knowledge. In this study, a freely available platform called Chemical Molecular Optimization, Representation and Translation (ChemMORT) is developed for the optimization of multiple ADMET endpoints without the loss of potency (https://cadd.nscc-tj.cn/deploy/chemmort/). ChemMORT contains three modules: Simplified Molecular Input Line Entry System (SMILES) Encoder, Descriptor Decoder and Molecular Optimizer. The SMILES Encoder can generate the molecular representation with a 512-dimensional vector, and the Descriptor Decoder is able to translate the above representation to the corresponding molecular structure with high accuracy. Based on reversible molecular representation and particle swarm optimization strategy, the Molecular Optimizer can be used to effectively optimize undesirable ADMET properties without the loss of bioactivity, which essentially accomplishes the design of inverse QSAR. The constrained multi-objective optimization of the poly (ADP-ribose) polymerase-1 inhibitor is provided as the case to explore the utility of ChemMORT. [ABSTRACT FROM AUTHOR]

A nonconvex distributed optimization problem involving nonconvex objective functions and inequality constraints within an undirected multi-agent network is considered. Each agent communicates with its neighbors while only obtaining its individual local information (i.e. its constraint and objective function information). To overcome the challenge caused by the nonconvexity of the objective function, a collective neurodynamic penalty approach in the framework of particle swarm optimization is proposed. The state solution convergence of every neurodynamic penalty approach is directed towards the critical point ensemble of the nonconvex distributed optimization problem. Furthermore, employing their individual neurodynamic models, each neural network conducts accurate local searches within constraints. Through the utilization of both locally best-known solution information and globally best-known solution information, along with the incremental enhancement of solution quality through iterations, the globally optimal solution for a nonconvex distributed optimization problem can be found. Simulations and an application are presented to demonstrate the effectiveness and feasibility. [ABSTRACT FROM AUTHOR]

With the rapid growth in cellular user quantity and quality of service demand, the resource allocation in device-to-device communication system significantly affects the overall efficiency and user experience. In this study, the resource allocation for large-scale device-to-device communication system is modelled as a constrained optimization problem with thousands of dimensionalities. Then, the variable-coupling relationship of the developed model is analysed and the mathematical proof is firstly provided, and a novel algorithm namely multi-modal mutation cooperatively coevolving particle swarm optimization is developed to optimize the ultra-high dimensional model. Finally, efficacy of the developed method is verified by a comprehensive set of case studies, some famous algorithms for the specialized literature are also employed for comparison. Experimental results shown that the developed algorithm can obtain accurate and robust optimization performance for different system scales. In addition, when the system scale increases to 1000 cellular users and 300 D2D-pair users, the developed method can still outperform the compared algorithms and output accurate resource allocation solution. [ABSTRACT FROM AUTHOR]

Minimizing energy consumption is often a grave challenge in many industrial energy-intensive process units such as Multiple Stage Evaporator (MSE). Integration of Energy Reduction Schemes (ERSs) is a common technique to resume a countable amount of energy. Hence, the present work proposes a hybrid (h-) ERSs integrated MSE placed in the paper industry, used to increase the solid content of the black liquor. The h-ERSs Integrated MSE comprises several ERSs such as Thermo-Vapor Compressor, Steam-, Feed-split, and Feed Preheater to improve the energy efficiency significantly, and its energy performance is compared with base (b-) MSE. For this purpose, nonlinear mathematical models have been developed and transformed into a constrained optimization problem to search for the optimum energy efficiency obtained as Steam Economy (SE). A state-of-art metaheuristic approach, Equilibrium Optimizer (EO), along with some well-acquainted solution approaches (Interior Point OPTimizer, Interior Point Method, and Particle Swarm Optimization) has been simulated in different platforms to estimate the maximum SE to check their competitiveness for this industrial optimization problem. It is observed that EO outperformed all the algorithms with a 66 % higher SE for h-MSE than b-MSE. Eventually, the steady state parameters are applied as the initial conditions to analyze the nonlinear enthalpy dynamics of the b-and h-MSE. A neural base solution has been adopted to rigorously study the open-loop process dynamics that meet the desired product quality. [ABSTRACT FROM AUTHOR]

Opposition-based learning (OBL) is often embedded in intelligent optimization algorithms to solve practical engineering and mathematical problems, but the combinatorial problems among different OBL variants are rarely studied. To this end, we propose a novel OBL variant based on the principle of optical imaging, which combines two novel types of quasi-opposite learning and extended opposite learning, called diversity-driven fused opposition learning (SQOBL). First, a density center based on a neighborhood model is proposed. Based on the rapid convergence of the centroid, combined the advantages of density and centroid to construct a double mean center (DMC) to replace the original center point in quasi-opposite learning based on the principle of refraction. Secondly, an extended opposite learning method based on optical refraction imaging is proposed. Diversity is then exploited to drive different opposing learning strategies at different stages of evolution, thus controlling the exploration and utilization of the algorithm. Finally, SQOBL was embedded in the PSO with eight others representative OBL variants to find the most optimal solution for a test suite. In addition, 8 novel intelligent optimization algorithms and the first three algorithms were selected to evaluate the performance of the latest CEC2022 benchmark test set and realistic constrained optimization problems. Experiments with 56 test functions and 3 real-world constraint optimization problems show that the proposed SQOBL has good integrative properties in CEC2015, CEC2017, CEC2020, and CEC2022 test suites. [ABSTRACT FROM AUTHOR]

In this work, we propose a variation of the cellular particle swarm optimization algorithm with differential evolution hybridization (CPSO-DE) to include constrained optimization, named Ts-CPD. It is implemented as a kernel of electronic design automation (EDA) tool capable of sizing circuit components considering a single-objective design with restrictions and constraints. The aim is to improve the optimization solutions in the sizing of analog circuits. To evaluate our proposal's performance, we present the design of three analog circuits: a differential amplifier, a two-stage operational amplifier (op-amp), and a folded cascode operational transconductance amplifier. Numerical simulation results indicate that Ts-CPD can find better solutions, in terms of the design objective and the accomplishment of constraints, than those reported in previous works. The Ts-CPD implementation was performed in Matlab using Ngspice and can be found on GitHub (see Data Availability Statement). [ABSTRACT FROM AUTHOR]

The quantum-inspired genetic algorithm (QGA), which combines quantum mechanics concepts and GA to enhance search capability, has been popular and provides an efficient search mechanism. This paper proposes a modified QGA, called dynamic QGA (DQGA). The proposed algorithm utilizes a lengthening chromosome strategy for a balanced and smooth transition between exploration and exploitation phases to avoid local optima and premature convergence. Apart from that, a novel adaptive look-up table for rotation gates is presented to boost the algorithm's optimization abilities. To evaluate the effectiveness of these ideas, DQGA is tested by various mathematical benchmark functions as well as real-world constrained engineering problems against several well-known and state-of-the-art algorithms. The obtained results indicate the merits of the proposed algorithm and its superiority for solving multimodal benchmark functions and real-world constrained engineering problems. [ABSTRACT FROM AUTHOR]

As a new bionic algorithm, Spider Monkey Optimization (SMO) has been widely used in various complex optimization problems in recent years. However, the new space exploration power of SMO is limited and the diversity of the population in SMO is not abundant. Thus, this paper focuses on how to reconstruct SMO to improve its performance, and a novel spider monkey optimization algorithm with opposition-based learning and orthogonal experimental design (SMO3) is developed. A position updatingmethod based on the historical optimal domain and particle swarm for Local Leader Phase (LLP) andGlobal Leader Phase (GLP) is presented to improve the diversity of the population of SMO. Moreover, an opposition-based learning strategy based on self-extremum is proposed to avoid suffering from premature convergence and getting stuck at locally optimal values. Also, a local worst individual elimination method based on orthogonal experimental design is used for helping the SMO algorithm eliminate the poor individuals in time. Furthermore, an extended SMO3 named CSMO3 is investigated to deal with constrained optimization problems. The proposed algorithm is applied to both unconstrained and constrained functions which include the CEC2006 benchmark set and three engineering problems. Experimental results show that the performance of the proposed algorithm is better than three well-known SMO algorithms and other evolutionary algorithms in unconstrained and constrained problems. [ABSTRACT FROM AUTHOR]

Trajectory planning with the involvement of motion time has become a key and challenge for autonomous systems. This paper investigates trajectory planning of unmanned aerial vehicles (UAVs) under maneuverability and collision avoidance constraints. First, a polynomial-based trajectory planning framework is established, and a nonlinear programming problem (NLP) is formulated. Then, a novel asymptotic optimization approach is proposed to improve NLP solution success rate. Three operations of dividing the original NLP into sub-problems, adding constraints gradually, and using previous NLP solution as current initial guess value are designed in the approach. Third, an improved particle swarm optimization (PSO) path planning is also proposed to generate initial guess value for the first sub-problem. Benefited from these operations, the NLP solution success rate is significantly improved. Finally, simulations on simultaneous attack of a same target are carried out. Comparisons with other algorithms illustrate the advantage of the proposed approach. [ABSTRACT FROM AUTHOR]

Economic dispatch problems (EDPs) can be reduced to non-convex constrained optimization problems, and most of the population-based algorithms are prone to have problems of premature and falling into local optimum when solving EDPs. Therefore, this paper proposes a hybrid quantum-behaved particle swarm optimization (HQPSO) algorithm to alleviate the above problems. In the HQPSO, the Solis and Wets local search method is used to enhance the local search ability of the QPSO so that the algorithm can find solutions that is close to optimal when the constraints are met, and two evolution operators are proposed and incorporated for the purpose of making a better balance between local search and global search abilities at the later search stage. The performance comparison is made among the HQPSO and the other ten population-based random search methods under two different experimental configurations and four different power systems in terms of solution quality, robustness, and convergence property. The experimental results show that the HQPSO improves the convergence properties of the QPSO and finally obtains the best total generation cost without violating any constraints. In addition, the HQPSO outperforms all the other algorithms on 7 cases of all 8 experimental cases in terms of global best position and mean position, which verifies the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

The ideal gas molecular movement (IGMM) as a metaheuristic optimization method is a prominent option for solving optimization problems. However, in some complex cases, IGMM may possess premature convergence or get trapped in local optima. Therefore, to tackle these issues, this paper indicates a new modified IGMM algorithm named opposition‐based IGMM, which has been incorporated with opposition based learning (OBL), Cauchy mutation (CM), velocity clamping (VC), and mirror operator (MO) to enhance its performance. OBL, VC, and MO improve the convergence of IGMM, whereas CM assists IGMM to escape local optima. The effect of each strategy, OBL, CM, VC, and MO, on IGMM, is confirmed through 30 low and high‐dimensional benchmarks, including 23 well‐known mathematical problems and CEC2017 as complex test functions and three engineering problems. Analysis results represent that integration IGMM with OBL, CM, VC, and MO has the best performance among other IGMM variants and eventually improved IGMM in exploration, exploitation, accelerating convergence, and local optima avoidance. [ABSTRACT FROM AUTHOR]

Particle swarm optimization (PSO) is a population-based swarm intelligence algorithm that falls under the category of nature-inspired algorithms and is similar to evolutionary computing in various ways. Rather than the survival of the fittest, the PSO is driven by a representation of a social psychological model inspired by the group behaviors of birds and other social species. The particle's position is modified in PSO based on its position as well as velocity, while in quantum mechanics, the trajectory idea is absurd; however, the uncertainty principle suggests that a particle's position, as well as velocity, cannot be determined simultaneously. As a result, an advanced version of the quantum mechanics-based PSO method is proposed. The study in this paper is focused on an investigation of a new quantum-behaved PSO (QPSO) method called Gaussian quantum-behaved particle swarm optimization (GQPSO), which uses a mutation operator with a Gaussian distribution and is inspired by classical PSO methods and quantum mechanics concepts. In GQPSO, inadequate control parameter tuning results in poor solutions. To better understand the effect of different control parameters and their implications on GQPSO results, this paper used a full parametric sensitivity analysis on five different problems (the Design of a pressure vessel, Tension/Spring Compression, Rastrigin function, Ackley function, and Constrained Box Volume Problem). By adjusting each parameter one at a time, different optimization problems were used to investigate GQPSO. As a result, to allow particles to change their earliest best solution based on viability, a constraint-handling mechanism was developed. The optimal parameter set for GQPSO is provided based on the analysis of the results. With the help of the proposed optimal parameter set (contraction–expansion coefficient values as (1 = 1.6,2 = 1.3), swarm size as '350', and number of Iterations as '500'), GQPSO returned an optimized solution for Rastrigin and Ackley functions. It also performed better in the case of the design of a pressure vessel and tension/spring compression problems in comparison to the existing solution available in related literature. As per the findings of the sensitivity analysis, GQPSO is the most sensitive to the contraction-expansion coefficient in comparison to the maximum number of iterations (itermax) and swarm size ('n'). [ABSTRACT FROM AUTHOR]

The paper proposes a novel nature-inspired optimization technique called Eagle Perching Optimizer (EPO). It is an addition to the family of swarm-based meta-heuristic algorithms. It mimics eagles’ perching nature to find prey (food). The EPO is based on the exploration and exploitation of an eagle when it descends from the height such that it formulates its trajectory in a way to get to the optimal solution (prey). The algorithm takes bigger chunks of search space and looks for the optimal solution. The optimal solution in that chunk becomes the search space for the next iteration, and this process is continuous until EPO converges to the optimal global solution. We performed the theoretical analysis of EPO, which shows that it converges to the optimal solution. The simulation includes three sets of problems, i.e., uni-model, multi-model, and constrained real-world problems. We employed EPO on the benchmark problems and compared the results with state-of-the-art meta-heuristic algorithms. For the real-world problems, we used a cantilever beam, three-bar truss, and gear train problems to test the robustness of EPO and later made the comparison. The comparison shows that EPO is comparable with other known meta-heuristic algorithms.

Particle Swarm Optimization (PSO) is a robust stochastic optimization algorithm for solving complex and constrained optimization problems. This paper aims to systematically investigate the influence of diverse random number distributions on the learning parameters of PSO and evaluate the algorithm's sensitivity based on these parameters. To this end, several exhaustive numerical experiments were conducted on several test functions, including asymmetric Sphere, Easom, and Csendes. Initially, appropriate random distributions were identified for the learning parameters and proceeded to compare these distributions using all potential combinations. The sensitivity of PSO to these parameters was evaluated using the mean best score, the relative proximity of the best particle to the theoretical optimum, and the average objective function evaluations. The most significant contribution of this research lies in the insights derived from the experimental data. The discovery was that highly skewed and long-tailed distributions such as Chi, Chi-square, Lomax, and Exponential often underperform. On the other hand, distributions that are bounded proportionally to the problem domain consistently surpass the performance of the standard uniform distribution. These findings significantly enhance the understanding of PSO algorithms and provide valuable guidelines for their practical deployment. [ABSTRACT FROM AUTHOR]

Bio‐inspired algorithms have become popular due to their capability of finding good solutions for complex optimization problems in an acceptable computational time. The Grey Wolf Optimizer is a nature‐inspired, population‐based metaheuristic that simulates the social hierarchy and the hunting strategy observed in a grey wolf pack. Although the Grey Wolf Optimizer has been successfully applied to solve different optimization problems, it may suffer from premature convergence and get stuck in local optima. In order to overcome these drawbacks, this paper proposes a chaotic version of the Grey Wolf Optimizer that differs from the original algorithm and previously published modified versions because it does not add a chaotic variable in the parameters that control the execution of the algorithm. Instead, the proposed model uses a chaotic variable to define the wolves in the pack that will be used to guide the hunting process in each iteration of the algorithm. Numerical experiments using 20 benchmark functions are carried out. The performance of the proposed model is compared with the performance of the original Grey Wolf Optimizer and other well‐known algorithms, namely the Particle Swarm Optimization, the Genetic Algorithm, the Symbiotic Organisms Search, and the Teaching‐Learning Based Optimization. Nine chaotic maps reported in the literature are tested. The results show that the proposed algorithm has a very competitive performance, and the Chebyshev map presented the best performance among the chaotic maps simulated. The proposed algorithm can be integrated into other modified versions of the Grey Wolf Optimizer in a straightforward way. [ABSTRACT FROM AUTHOR]

Wind farm layout optimization (WFLOP) is a complex constrained optimization problem. In this paper, an application of biogeography based optimization (BBO) algorithm for large WFLOP is presented and its performance is compared against three other major meta-heuristic algorithms namely genetic algorithm (GA), particle swarm optimization (PSO), and ant colony optimization (ACO). The performance is evaluated on the basis of a modified cost of energy fitness function across 25 different wind farm scenarios. Further statistical inference has been drawn on the basis of 99% Confidence Intervals and Student's t-test which shows that the fitness function values obtained using BBO are better when compared to the other algorithms for the same number of iterations. The execution time in case of PSO is largest, while GA takes the lowest amount of time. [ABSTRACT FROM AUTHOR]

Crude Oil Distillation Unit (CDU) is one of the most important separation installations in the petroleum refinery industries. In this work, a Bi-level Surrogate column model Aided Constrained Optimization Design (Bi-SACOD) is proposed for time-consuming objectives and constraints in the evolutionary optimization design of CDUs. The main components of Bi-SACOD include bi-level surrogate model construction (Bi-SMC), bi-level model management (Bi-MM), and particle swarm optimization (PSO) mixed-integer constrained evolutionary (PSO-MICE) search. Bi-SMC implements surrogate column model construction and feasible domain identification. Bi-MM combines surrogate column models with rigorous CDU simulations to perform model management, and PSO-MICE implements optimum search works. The optimization results of the CDUs indicate that Bi-SACOD outperforms the single-level surrogate column model approaches, and are more consistent with the rigorous CDU model optimization approach, whereas the evaluation numbers of the timeconsuming rigorous models are significantly reduced. [ABSTRACT FROM AUTHOR]

In this paper, we study swarm intelligence computation for constrained optimization problems and propose a new hybrid PSO-DE algorithm based on feasibility rules. Establishing individual feasibility rules as a way to determine whether the position of an individual satisfies the constraint or violates the degree of the constraint, which will determine the choice of the individual optimal position and the global optimal position in the particle population. First, particle swarm optimization (PSO) is used to act on the top 50% of individuals with higher degree of constraint violation to update their velocity and position. Second, Differential Evolution (DE) is applied to act on the individual optimal position of each individual to form a new population. The current individual optimal position and the global optimal position are updated using the feasibility rules, thus forming a hybrid PSO-DE intelligent algorithm. Analyzing the convergence and complexity of PSO-DE. Finally, the performance of the PSO-DE algorithm is tested with 12 benchmark functions of constrained optimization and 57 engineering optimization problems, the numerical results show that the proposed algorithm has good accuracy, effectiveness and robustness. [ABSTRACT FROM AUTHOR]

Crow Search Algorithm (CSA) is a promising meta-heuristic method developed based on the intelligent conduct of crows in nature. This algorithm lacks a good representation of its individuals' memory, and as with many other meta-heuristics it faces a problem in efficiently balancing exploration and exploitation. These defects may lead to early convergence to local optima. To cope with such issues, we proposed a Memory based Hybrid CSA (MHCSA) with the use of Particle Swarm Optimization (PSO) algorithm. This hybridization approach was proposed to reinforce the diversity ability of CSA and balance its search abilities for promising solutions to achieve robust search performance. The memory element of MHCSA was initialized with the best solution (pbest) of PSO to exploit the most promising search areas. The best positions of the CSA's individuals are improved using the best solution found so far (gbest) and (pbest) of PSO. Another flaw of CSA is the use of fixed flight length and awareness probability for crows to control exploration and exploitation features, respectively. This issue was circumvented here by replacing these constants with adaptive functions in order to provide a better balance between exploration and exploitation over the course of iterations. The competence of MHCSA was revealed by testing it on seventy-three standard and computationally complex benchmark functions. Its applicability was substantiated by solving seven engineering design problems. The results showed that the problem of early convergence was eliminated by MHCSA and that the balance of exploration and exploitation was further improved. Further, MHCSA ranked first among CSA, PSO, robust variants of CSA and other strong competing methods in terms of accuracy and stability. [ABSTRACT FROM AUTHOR]

In this work, we propose a variation of the cellular particle swarm optimization algorithm with differential evolution hybridization (CPSO-DE) to include constrained optimization, named Ts-CPD. It is implemented as a kernel of electronic design automation (EDA) tool capable of sizing circuit components considering a single-objective design with restrictions and constraints. The aim is to improve the optimization solutions in the sizing of analog circuits. To evaluate our proposal’s performance, we present the design of three analog circuits: a differential amplifier, a two-stage operational amplifier (op-amp), and a folded cascode operational transconductance amplifier. Numerical simulation results indicate that Ts-CPD can find better solutions, in terms of the design objective and the accomplishment of constraints, than those reported in previous works. The Ts-CPD implementation was performed in Matlab using Ngspice and can be found on GitHub (see Data Availability Statement).

Summary: Recently, workflow applications are increasingly migrated to Function‐as‐a‐Service platforms which are easy to manage, highly‐scalable, and pay‐as‐you‐go. Meanwhile, users face challenges in migration of serverless applications because of the lack of efficient algorithm for workflow memory configuration to optimize the performance. To this end, this article proposes a heuristic urgency‐based algorithm UWC and a meta‐heuristic hybrid algorithm BPSO to tackle the time‐cost tradeoff. UWC sorts functions and allocates each function an appropriate memory size by greedy strategy. BPSO hybridizes particle swarm optimization as well as beetle antennae search algorithm to guide particles to search directionally and utilizes nonlinear inertia weight to avoid local premature convergence. Extensive experiments with classical serverless application demonstrate that UWC and BPSO are very competitive in comparison with existing algorithms as they can find the optimal workflow memory configuration. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to present an improved neural network algorithm (INNA) for solving the reliability-redundancy allocation problem (RRAP) with nonlinear resource constraints. In this RRAP, both the component reliability and the redundancy allocation are to be considered simultaneously. Neural network algorithm (NNA) is one of the newest and efficient swarm optimization algorithms having a strong global search ability that is very adequate in solving different kinds of complex optimization problems. Despite its efficiency, NNA experiences poor exploitation, which causes slow convergence and also restricts its practical application of solving optimization problems. Considering this deficiency and to obtain a better balance between exploration and exploitation, searching procedure for NNA is reconstructed by implementing a new logarithmic spiral search operator and the searching strategy of the learner phase of teaching–learning-based optimization (TLBO) and an improved NNA has been developed in this paper. To demonstrate the performance of INNA, it is evaluated against seven well-known reliability optimization problems and finally compared with other existing meta-heuristics algorithms. Additionally, the INNA results are statistically investigated with the Wilcoxon sign-rank test and Multiple comparison test to show the significance of the results. Experimental results reveal that the proposed algorithm is highly competitive and performs better than previously developed algorithms in the literature. [ABSTRACT FROM AUTHOR]

This paper presents a multi-objective version of the artificial vultures optimization algorithm (AVOA) for a multi-objective optimization problem called a multi-objective AVOA (MOAVOA). The inspirational concept of the AVOA is based on African vultures' lifestyles. Archive, grid, and leader selection mechanisms are used for developing the MOAVOA. The proposed MOAVOA algorithm is tested oneight real-world engineering design problems and seventeen unconstrained and constrained mathematical optimization problems to investigates its appropriateness in estimating Pareto optimal solutions. Multi-objective particle swarm optimization, multi-objective ant lion optimization, multi-objective multi-verse optimization, multi-objective genetic algorithms, multi-objective salp swarm algorithm, and multi-objective grey wolf optimizer are compared with MOAVOA using generational distance, inverted generational distance, maximum spread, and spacing performance indicators. This paper demonstrates that MOAVOA is capable of outranking the other approaches. It is concluded that the proposed MOAVOA has merits in solving challenging multi-objective problems. [ABSTRACT FROM AUTHOR]

The present work proposes a multi-stage optimization technique for damage detection of the thin plate-like structure using sparse modal data collected from the limited sensors. The response of the thin plate structures is obtained from a finite element model, which is developed with constant strain triangle elements. The iterated improved reduction system (IIRS) is applied to simulate the state of sparse sensors numerically. Next, the damage identification problem is formulated as a constrained optimization problem. A weighted linear combination of the natural frequencies and modes shapes of the structure is used to construct the objective function. The damage extent of each element is considered as the design variable. The formulated objective function is minimized by the proposed multi-stage strategy employing a hybrid metaheuristic algorithm, which is based on particle swarm optimization and gravitational search algorithm (PSOGSA). The algorithm identifies the healthy elements in each stage and eliminates them from the search space of the next stage. Thus, the search space dimension of the optimization problem reduces in each stage, and actual damaged elements are identified effectively. The efficiency of the proposed method is demonstrated through three numerical examples with different damage scenarios. [ABSTRACT FROM AUTHOR]

A periodic close encounter orbit is an orbit that periodically surveys a target spacecraft. The design of such orbits is important for both military and civil missions. As an example, it can be used for a preliminary mission design to better plan the repair of damaged, expensive, strategic, or extremely critical space assets. Indeed, this paper proposes an optimization approach and algorithm to reach periodic close encounter orbits through n -impulse maneuvers within the framework of Keplerian motion. The optimization is constrained to several bound requirements, such as a) distance bounds at encounter, b) total Δ v tot cost, c) single impulse cost Δ v k , d) encounter illumination angle, e) daily observation time, f) minimum perigee radius, g) maximum repetition time, h) maximum eccentricity, and i) maximum encounter time. The optimization, performed using Genetic Algorithm, has been validated by several numerical examples using the TLEs of existing satellites. The simulations showed good results and the reliability of the proposed approach via the Genetic Algorithm has been validated by a comparison with the Particle Swarm Optimization algorithm. [ABSTRACT FROM AUTHOR]

When solving constrained optimization problems (COPs) with high-dimension and multi-extreme problems, genetic algorithm (GA) has the issue of trapping into local optimum. Therefore, this paper proposes a hybrid real-coded genetic algorithm (HIRCGA). First, a sorting group selection (SGS) is given, which is a simple operation and easy to implement. Second, a combinational crossover (CX) operator is developed. It consists of a heuristic normal distribution and direction crossover based on the optimal individual (HNDDX-BOI) and a sine cosine crossover (SCX), which enhances the exploration ability of the algorithm. Third, an operation of eliminating the similarity of different variables in the same dimension (ES) is added, which significantly avoids premature convergence and maintains the population diversity. Fourth, a combinational mutation (CM) operator is proposed, where the global and local search abilities of HIRCGA are fully considered. Fifth, the chaotic search (CS) based on Tent map is introduced to enhance the search power of HIRCGA. Moreover, 28 benchmark test functions in CEC 2017 and two complex real-world optimization problems are selected to demonstrate the effectiveness and superiority of HIRCGA. The computational results and statistical analysis indicate that HIRCGA can improve the solution accuracy compared with other algorithms. The effectiveness of HIRCGA is verified in theory and practice. [ABSTRACT FROM AUTHOR]

The concept of computational optimization can be used to solve linear and nonlinear Diophantine equations. The goal of this article is to introduce an alternative technique, which is independent of greatest common divisor (g.c.d), to solve linear and nonlinear Diophantine equations by transforming the equations into discrete variable bound constrained optimization problems. To solve these optimization problems, weighted quantum-behaved particle swarm optimization (WQPSO) is developed into discrete variable form. Then, four linear and four nonlinear Diophantine equations are considered from recent literature and solved by using the discrete WQPSO algorithm. Next, the convergence histories of each of the problems are shown. Finally, the results are compared with the same reported by numerous researchers. [ABSTRACT FROM AUTHOR]

This article presents design optimization based on swarm intelligence algorithms of a tubular linear voice coil motor (TLVCM). A magnetic equivalent circuit model is used, allowing a faster and more accurate evaluation of the initial design of the TLVCM. The design requirements are determined, and an initial design is formed based on the design requirements. The TLVCM design is considered a constrained optimization problem with complex linear and nonlinear constraints. The optimization process based on swarm intelligence algorithms is performed to find the optimal solution and improve the performance of the TLVCM. Finally, finite element analysis is used again to verify the optimized results, and different design outputs are compared. According to numerical experimental results, the average thrust is increased by 8.3% and the thrust ripple is reduced by 35.6%. Thus, a highly effective motor design meeting efficiency and performance requirements is achieved. [ABSTRACT FROM AUTHOR]

The objective of a fixed-cost transportation problem (FCTP) is to minimize the total transportation cost (or to maximize the total profit). FCTPs become more complex due to the presence of uncertain factors like suppliers' capacity, customers' demand, fixed cost and amount of goods transported. In order to deal with its complexity, three mathematical models, namely the expected value, chance-constrained and measure chance models, are basically available in the literature. This paper aims to solve the crisp equivalent models of an uncertain two-echelon FCTP by employing a robust hybrid evolutionary algorithm. The framework of the designed algorithm comprises of parameter-free Jaya algorithm and the efficient quadratic approximation operator. Initially, its efficiency is validated on the 24 constrained benchmark functions from CEC 2006 (Liang et al. in Problem definitions and evaluation criteria for the CEC 2006 special session on constrained real-parameter optimization, 2006) and 30 unconstrained benchmark functions (Rao in Int J Ind Eng Comput 7:19–34, 2016). Friedman rank test and Iman–Davenport test are conducted for both constrained and unconstrained test instances. Additionally, five real-life engineering design problems are solved to further analyze the flexibility of the proposed algorithm. The computational results and convergence graphs justify the robustness of the designed algorithm over state-of-the-art algorithms. Finally, its application is demonstrated on chance-constrained and measure chance two-echelon FCTP models. Near-optimal results are contradicted by genetic algorithm and particle swarm optimization in solving both these models. Further, a few large-scale instances of FCTP are solved by the proposed algorithm. [ABSTRACT FROM AUTHOR]

Distributed evolutionary computation has been efficiently used, in last decades, to solve complex optimization problems. Island model (IM) is considered as a distributed population paradigm employed by evolutionary algorithms to preserve the diversification and, thus, to improve the local search. In this article, we study different island model techniques integrated in to particle swarm optimization (PSO) algorithm in order to overcome its drawbacks: premature convergence and lack of diversity. The first IMPSO approach consists in using the migration process in a static way to enhance the police migration strategy. On the other hand, the second approach, called dynamic-IMPSO, consists in integrating a learning strategy in case of migration. The last version called constrained-IMPSO utilizes a stochastic technique to ensure good communication between the sub-swarms. To evaluate and verify the effectiveness of the proposed algorithms, several standard constrained and unconstrained benchmark functions are used. The obtained results confirm that these algorithms are more efficient in solving low-dimensional problems (CEC'05), large-scale optimization problems (CEC'13) and constrained problems (CEC'06), compared to other well-known evolutionary algorithms. [ABSTRACT FROM AUTHOR]

Since Internet of Everything (IoE) makes all the connections that come online more relevant and valuable, they are subject to numerous security and privacy concerns. Cybersecurity ontology is a shared knowledge model for tackling the security information heterogeneity issue on IoE, which has been widely used in the IoE domain. However, the existing CSOs are developed and maintained independently, yielding the CSO heterogeneity problem. To address this issue, we need to use the similarity measure (SM) to calculate two entities' similarity value in two CSOs and, on this basis, determine the entity correspondences, i.e., CSO alignment. Usually, it is necessary to integrate various SMs to enhance the result's correctness, but how to combine and tune these SMs to improve the alignment's quality is still a challenge. To face this challenge, this work first models CSO matching problem as a Constrained Multiobjective Optimization Problem (CMOOP) and then proposes a Coevolutionary Multiobjective Evolutionary Algorithm (CE-MOEA) to effectively address it. In particular, CE-MOEA uses the multiobjective evolutionary paradigm to avoid the solutions' bias improvement and introduces the coevolutionary mechanism to trade off Pareto Front's (PF's) diversity and convergence. The experiment uses Ontology Alignment Evaluation Initiative's (OAEI's) bibliographic track and conference track and five real CSO matching tasks to test CE-MOEA's performance. Comparisons between OAEI's participants and EA- and MOEA-based matching techniques show that CE-MOEA is able to effectively address various heterogeneous ontology matching problems and determine high-quality CSO alignments. [ABSTRACT FROM AUTHOR]

This paper proposes a modified quasi-opposition-based grey wolf optimization (mQOGWO) method to solve complex constrained optimization problems. The effectiveness of mQOGWO is examined on (i) 23 mathematical benchmark functions with different dimensions and (ii) four practical complex constrained electrical problems that include economic dispatch of 15, 40, and 140 power generating units and a microgrid problem with different energy sources. The obtained results are compared with the reported results using other methods available in the literature. Considering the solution quality of all test cases, the proposed technique seems to be a promising alternative for solving complex constrained optimization problems. [ABSTRACT FROM AUTHOR]

The paper presents the efficiency optimization of the Hybrid Energy Storage System (HESS) of an Electric Vehicle (EV), composed of a battery and a supercapacitor. The efficiency optimization is carried out by searching the optimal power sharing between the energy storage devices, reducing to a minimum the energy losses. The correct operation of the HESS is subject to several constraints, out of which six have been considered in the optimization problem. The constrained optimization has been performed using the Particle Swarm Optimization (PSO) algorithm, first tested on the Rastrigin function. The implementation of this stochastic optimization method can be a challenge even in low-dimensional cases, due to the non-convex feasible solution space. The paper presents a modified version of the basic PSO algorithm, which can initialize the particle swarm, and find the minimum of the cost function even in very narrow, „needle-like", non-connex domains. The modified PSO algorithm has been tested for the HESS of the EV on a single segment of a standard driving cycle, composed of an acceleration and a braking time interval. [ABSTRACT FROM AUTHOR]

Multi-objective optimization problems have been widely studied in the evolutionary computation domain, and many algorithms with evolutionary computation have been successfully adopted to solve multi-objective optimization problems. The information feedback model is an effective method to generate new individuals by combining previous and current information. It has been successfully used to improve the performance of particle swarm optimization to solve single-objective optimization problems. However, the current information feedback model has at least two drawbacks. First, the function evaluation number is increased for all individuals should be evaluated twice in a generation. Second, the model cannot directly be used to solve the multi-objective optimization problems because the update coefficient of the feedback model is difficult to determine for many objectives. To solve these problems, an information feedback model based on mutual information is developed to directly solve the multi-objective optimization problems. The model can be easily combined with any optimization algorithm. The model was used in particle swarm optimization to form a new multi-objective particle swarm optimization in this study. In this method, the external archive of multi-objective particle swarm optimization was built by non-dominated sorting method, the basic operator of multi-objective particle swarm optimization and the information feedback model based on mutual information were used to update individuals with different characters. The convergence and diversity of the population were better balanced. The effectiveness of the method was tested on non-constrained, constrained, and real-world problems. The results of the method were compared to those of some state-of-the-art multi-objective optimization problems. The comparisons indicate that the proposed algorithm is challenging in terms of the generational distance and spacing of solutions in multi-objective optimization problems. [ABSTRACT FROM AUTHOR]

This paper presents an algorithm and optimization procedure for the optimization of the outer rotor structure of the brushless DC (BLDC) motor. The optimization software was developed in the Delphi Tiburón development environment. The optimization procedure is based on the salp swarm algorithm. The effectiveness of the developed optimization procedurewas compared with genetic algorithm and particle swarmoptimization algorithm. The mathematical model of the device includes the electromagnetic field equations taking into account the non-linearity of the ferromagnetic material, equations of external supply circuits and equations of mechanical motion. The external penalty function was introduced into the optimization algorithm to take into account the non-linear constraint function. [ABSTRACT FROM AUTHOR]

Environmental concerns motivate the researchers to consider the pollutants emission as an important objective in the cogeneration‐based unit commitment problem (CBUCP). Hence, single objective CBUCP has been extended as a multi‐objective optimization problem (MOP). In the MO CBUC problem (MO‐CBUCP), in which operating costs are optimized along with pollutants. The CBUCP is a nonlinear, mixed‐integer, and highly constrained optimization problem. The nature‐inspired binary and continuous particle swarm optimization (BPSO‐PSO) approach have been applied to deal with binary (ON/OFF) and real variables of the CBUCP, respectively. To attain a global best solution with the minimum CPU burden, an enhanced optimization approach (EOA) based on the combination of conventional and global optimization techniques is also applied. The enhanced successive approximation approach (SAA) is applied to deal with binary variables of the UC problem and global search is employed to search continuous variables. In the MOP, a fuzzy membership approach unifies the different objectives of MO‐CBUCP. The decision‐making method decides the best‐satisfied non‐dominated solution among Pareto optimal solutions. The MO‐CBUCP test system has been undertaken and results reveal that the 'EOA' technique is superior as compared to the BPSO‐PSO approach. It has been examined from results that cogeneration unit has a significant impact on operating cost and pollutants emission of the cogeneration system. [ABSTRACT FROM AUTHOR]

In this paper, for the remotely operated vehicle (ROV) system, a cascaded dynamics trajectory tracking controller for thruster fault is proposed. Firstly, the control strategies are used to reallocate the thruster forces based on quantum-behaved particle swarm optimization (QPSO). Secondly, the kinematic controller based on QPSO-model predictive control (MPC) is designed to obtain the speed control signal. QPSO-MPC with speed constrained optimization is proposed to solve speed jump problem caused by thruster failure. Finally, the kinematic controller method is used in conjunction with dynamic controller based on the sliding mode control (SMC) to achieve dynamic tracking control, and to solve the driving saturation problem caused by thruster failure. Experimental results showed the proposed dynamics trajectory tracking controller could realize trajectory tracking problem under thruster failure without the driving saturation. [ABSTRACT FROM AUTHOR]

The presence of shadows has always been a troublesome problem in image processing and can also affect spectral unmixing with hyperspectral remote sensing images. Traditional unmixing algorithms regard shadows as a special type of ground cover, so they can only estimate real materials’ sunlit abundances, and materials with low reflectance may be wrongly recognized as shadows. Without regarding shadows as ground cover, we propose a supervised nonlinear unmixing method to accurately estimate real materials’ total abundances inside and outside shadow areas. First, sunlit and shadowed constituents in every pixel of an image are modeled explicitly and integrated by a tractable bilinear mixing mechanism. Second, based on the constructed model, the strong sparsity of shadow spatial distribution and the spatial correlation among neighboring material abundances are exploited to produce a constrained optimization problem for nonlinear unmixing. Third, an existing unmixing framework based on particle swarm optimization is extended to calculate unknown variables of the optimization problem. Three alternatingly updated swarms using improved dimensional division strategies are designed to accordingly address the unmixing subproblems with respect to variables to be estimated during the solution search. This process has the potential to be generalized to solve complex nonlinear unmixing optimization problems. Finally, model-based simulated data, virtual citrus orchard data, and real hyperspectral remote sensing images are used in experiments to evaluate the proposed method and compare it with traditional and state-of-the-art nonlinear unmixing algorithms. Experimental results verify that the proposed method can achieve acceptable unmixing performance when managing nonlinear mixing effects and shadows. [ABSTRACT FROM AUTHOR]