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An enhanced surrogate-assisted differential evolution for constrained optimization problems

SOFT COMPUTING 2023年第10期27卷 6391-6414页

作者： Garcia, Rafael de Paula de Lima, Beatriz Souza Leite Pires Lemonge, Afonso Celso de Castro Jacob, Breno Pinheiro Fed Univ Rio Janeiro PEC COPPE UFRJ Post Grad Inst Civil Engn Dept Ave Pedro Calmon S-N BR-21941596 Rio De Janeiro RJ Brazil UFJF Fed Univ Juiz Fora Appl & Computat Mech Dept Rua Jose Lourenco Kelmer S-N BR-36036330 Juiz De Fora MG Brazil UFV Fed Univ Vicosa Dept Architecture & Urban Planning Campus UnivAve Peter Henry Rolfs S-N BR-36570900 Vicosa MG Brazil

The application of evolutionary algorithms (EAs) to complex engineering optimization problems may present difficulties as they require many evaluations of the objective functions by computationally expensive simulation procedures. To deal with this issue, surrogate models have been employed to replace those expensive simulations. In this work, a surrogate assisted evolutionary optimization procedure is proposed. The procedure combines the differential evolution method with a k-nearest neighbors (k-NN) similarity-based surrogate model. In this approach, the database that stores the solutions evaluated by the exact model, which are used to approximate new solutions, is managed according to a merit scheme. Constraints are handled by a rank-based technique that builds multiple separate queues based on the values of the objective function and the violation of each constraint. Also, to avoid premature convergence of the method, a strategy that triggers a random reinitialization of the population is considered. The performance of the proposed method is assessed by numerical experiments using 24 constrained benchmark functions and 5 mechanical engineering problems. The results show that the method achieves optimal solutions with a remarkably reduction in the number of function evaluations compared to the literature.

关键词： constrained optimization problems Evolutionary algorithms Surrogate models Constraint-handling techniques

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A league-knock-out tournament quantum particle swarm optimization algorithm for nonlinear constrained optimization problems and applications

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EVOLVING SYSTEMS 2023年第6期14卷 1117-1143页

作者： Mandal, Goutam Kumar, Nirmal Duary, Avijit Shaikh, Ali Akbar Bhunia, Asoke Kumar Univ Burdwan Dept Math Purba Burdwan 713104 West Bengal India Purulia Polytech Purulia 723147 West Bengal India Maulana Abul Kalam Azad Univ Technol Dept Appl Math Haringhata 741249 West Bengal India

In solving optimization problem by metaheuristic algorithm, the main key for avoiding local optima is to enhance the exploration and exploitation phases. This is possible through several processes like enhancement of search agent, hybridization etc. The aim of the work is to introduce a novel hybrid algorithm considering the strategies of group league and knock-out system through advanced metaheuristic algorithm, viz. quantum particle swarm optimization (QPSO). In the tournament of well known games, like Football World Cup, Cricket World Cup, etc. the above average teams (according to their performance) are selected through group league phase in the first round. Then the best team is identified through knock-out phase in the next rounds. This concept is applied in the proposed work to develop a hybrid algorithm for solving non-convex constrained optimization problems using parameter free penalty function technique. In group league phase, five different strategies are considered in developing league-knock-out based hybrid algorithms. For testing the robustness as well as efficiencies of these algorithms, constrained benchmark problems are solved and the results are compared with the existing popular algorithms. The obtained results of different variants are compared statistically and graphically to pick up the best strategy based algorithm. To analyse the statistical significance of the results obtained by hybrid algorithms, three statistical tests (non-parametric) are performed. Finally, this best strategy based hybrid algorithm is applied fruitfully for solving several engineering design problems.

关键词： Particle swarm optimization (PSO) Hybrid algorithm Metaheuristic algorithm constrained optimization problems Engineering design problems Non-parametric statistical test

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An optimization numerical spiking neural P system for solving constrained optimization problems

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INFORMATION SCIENCES 2023年第1期626卷 428-456页

作者： Dong, Jianping Zhang, Gexiang Luo, Biao Rong, Haina Chengdu Univ Technol Sch Nucl Technol & Automat Engn Chengdu 610059 Peoples R China Chengdu Univ Technol Res Ctr Artificial Intelligence Chengdu 610059 Peoples R China Chengdu Univ Informat Technol Sch Automat Chengdu 610225 Peoples R China Southwest Jiaotong Univ Sch Elect Engn Chengdu 611756 Peoples R China

An optimization spiking neural P (OSN P) system is a discrete optimization model without the aid of evolutionary operators of evolutionary algorithms or swarm intelligence algorithms. However, since the processing object of OSN P systems is a spike, where information is encoded by the timing of spikes or the number of spikes in neurons, OSN P systems are limited for solving continuous optimization problems. To break this limitation, an extended numerical spiking neural (ENSN P) system is proposed based on numerical spik-ing neural P (NSN P) systems and multiple (ENSN P) systems, called optimization numer-ical spiking neural P systems (ONSN P systems or ONSNPS), are designed to solve continuous constrained optimization problems. More specifically, in ENSN P systems, the production functions are selected by probability to achieve updated parameters. In OSN P systems, a guider algorithm is introduced to finish individuals' crossover and selection. The extensively experimental results in five benchmarks, thirty-two optimization prob-lems including five benchmark problems, seventeen manufacturing design optimization problems and ten benchmarks from CEC show that ONSN P systems in this paper outper-form or are competitive to twenty-eight optimization algorithms. Finally, algorithm complexity and Holm-Bonferroni procedure based on statistical results is used to test the complexity changing when we use different dimensionality of the search space and the dif-ference in terms of statistical performance. The testing results indicate that the time complexity of ONSN P systems grows linearly with problem dimensions and ONSN P systems are better performance than the most algorithms.(c) 2023 Elsevier Inc. All rights reserved.

关键词： constrained optimization problems Spiking neural P system Numerical spiking neural P systems optimization numerical spiking neural P system Membrane computing

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A particle swarm optimization algorithm based on an improved deb criterion for constrained optimization problems

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PEERJ COMPUTER SCIENCE 2022年 8卷 e1178页

作者： Sun, Ying Shi, Wanyuan Gao, Yuelin North Minzu Univ Collaborat Innovat Ctr Sci Comp & Intelligent Pro Yinchuan Ningxia Peoples R China North Minzu Univ Sch Math & Informat Sci Yinchuan Ningxia Peoples R China

To solve the nonlinear constrained optimization problem, a particle swarm optimization algorithm based on the improved Deb criterion (CPSO) is proposed. Based on the Deb criterion, the algorithm retains the information of 'excellent' infeasible solutions. The algorithm uses this information to escape from the local best solution and quickly converge to the global best solution. Additionally, to further improve the global search ability of the algorithm, the DE strategy is used to optimize the personal best position of the particle, which speeds up the convergence speed of the algorithm. The performance of our method was tested on 24 benchmark problems from IEEE CEC2006 and three real-world constraint optimization problems from CEC2020. The simulation results show that the CPSO algorithm is effective.

关键词： Particle swarm optimization algorithm constrained optimization problems Deb criterion

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Comprehensive learning Harris hawks-equilibrium optimization with terminal replacement mechanism for constrained optimization problems

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EXPERT SYSTEMS WITH APPLICATIONS 2022年第0期192卷 116432-116432页

作者： Zhong, Changting Li, Gang Dalian Univ Technol Dept Engn Mech State Key Lab Struct Anal Ind Equipment Dalian 116024 Peoples R China

Harris hawks optimization (HHO) is a novel metaheuristic algorithm which has strong convergence for unconstrained optimization problems. However, HHO may encounter premature or local stagnation for constrained optimization problems. In this paper, a hybrid HHO algorithm named comprehensive learning harris hawks equilibrium optimization (CLHHEO) is presented for solving constrained optimization problems, with the help of three operators: comprehensive learning, equilibrium optimizer, and terminal replacement mechanism. In the proposed algorithm, comprehensive learning strategy is incorporated with HHO to make search agents share their knowledge to enhance the convergence capacity. The operator of equilibrium optimizer is utilized to improve the exploration capacity of HHO. Besides, the terminal replacement mechanism is incorporated in the proposed algorithm to avoid local stagnation. The proposed CLHHEO is tested on 15 unconstrained and 10 real world constrained optimization problems, and compared with 10 state-of-the-art metaheuristic algorithms, including PSO, CLPSO, BBBC, GWO, DA, WOA, SSA, HHO, SOA and AOA. From the experimental results, it is observed that CLHHEO outperforms HHO and other comparing metaheuristic algorithms in terms of solution quality. The results also demonstrate that the ensemble strategies of CLHHEO can enhance the performance of HHO for constrained optimization problems.

关键词： Metaheuristics Harris hawks optimization Equilibrium optimizer Comprehensive learning constrained optimization problems

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Symbiotic Organisms Search for constrained optimization problems

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JOURNAL OF INFORMATION PROCESSING SYSTEMS 2020年第1期16卷 210-223页

作者： Wang, Yanjiao Tao, Huanhuan Ma, Zhuang Northeast Elect Power Univ Sch Elect Engn Jilin Jilin Peoples R China

Since constrained optimization algorithms are easy to fall into local optimum and their ability of searching are weak, an improved symbiotic organisms search algorithm with mixed strategy based on adaptive e constrained (epsilon_SOSMS) is proposed in this paper. Firstly, an adaptive e constrained method is presented to balance the relationship between the constrained violation degrees and fitness. Secondly, the evolutionary strategies of symbiotic organisms search algorithm are improved as follows. Selecting different best individuals according to the proportion of feasible individuals and infeasible individuals to make evolutionary strategy more suitable for solving constrained optimization problems, and the individual comparison criteria is replaced with population selection strategy, which can better enhance the diversity of population. Finally, numerical experiments on 13 benchmark functions show that not only is epsilon_SOSMS able to converge to the global optimal solution, but also it has better robustness.

关键词： constrained optimization problems epsilon constrained Symbiotic Organisms Search

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An active-learning probabilistic neural network for feasibility classification of constrained engineering optimization problems

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ENGINEERING WITH COMPUTERS 2022年第SUPPL 4期38卷 3237-3250页

作者： Fang, Dengjian Zhang, Taotao Wu, Fan Naval Univ Engn Coll Weap Engn Wuhan 430033 Peoples R China Naval Univ Engn Coll Naval Architecture & Ocean Engn Wuhan 430033 Peoples R China

constrained optimization problems (COPs) with multiple computational expensive constraints are commonly encountered in simulation-based engineering designs. During the optimization process, the feasibility analysis of the intermediate solutions depends on the computational simulations will be computationally prohibitive. To relieve this computational burden, an active-learning probabilistic neural network (AL-PNN) classification modeling approach is proposed to build a classifier for quickly analyzing the feasibility of the intermediate solutions. In the proposed AL-PNN approach, an interesting region tracking strategy is developed to locate the regions that may contain part of the constraint boundary. The judge rule of the interesting region is based on whether the predicted class labels of pseudo points are different in subregions, which is generated by dividing the design space with the K-means cluster algorithm. Once the interesting region is located, the newly infill sample point used to update the PNN classification model can be obtained by a distance screening criterion. Seven numerical cases and the design of the rocket interstage section are used to demonstrate the performances of the proposed approach. The results illustrate that the proposed AL-PNN approach can provide more accurate classification results than the compared four state-of-the-art algorithms.

关键词： Computational expensive constraints Probabilistic neural network Active learning constrained optimization problems

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A Modification of the Imperialist Competitive Algorithm With Hybrid Methods for constrained optimization problems

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IEEE ACCESS 2021年 9卷 161745-161760页

作者： Luo, Jianfu Zhou, Jinsheng Jiang, Xi China Univ Geosci Sch Econ & Management Beijing 100083 Peoples R China

This paper studies a modification of the imperialist competitive algorithm to solve constrained optimization problems with hybrid methods. The imperialist competitive algorithm is a kind of evolutionary algorithm based on the colonial competition mechanism of imperialism, which is a type of social heuristic optimization algorithm. However, this algorithm needs to be modified because of some problems, including the decreasing number of empires, which leads to easily falling into a local optimum, and a lack of information exchange among countries leading to the search ability being insufficiently strong. To solve these problems and improve the search ability of the algorithm, a newly improved imperialist competitive algorithm with hybrid methods is proposed based on the imperialist competitive algorithm in this paper. In this algorithm, a crossover mechanism is introduced as the empire interaction to boost the information exchange among countries. Two groups of combinatorial operators are constructed in the process of empire assimilation and revolution to improve the algorithm's global and local search ability. Moreover, a step of empire splitting is added based on the imperialist competitive algorithm in which the number of empires is decreased rapidly by empire competition in imperialist competitive algorithm, which leads to a decrease in the population diversity. Then, the proposed algorithm is tested on 12 benchmark functions and 4 engineering problems, and the constraints are addressed by Deb's rules. The results obtained by the proposed algorithm are compared with those of algorithms that have obtained good results in recent years, and it is shown that the proposed method obtains promising and comparable results on the constrained optimization benchmarks in terms of the solution quality and robustness.

关键词： optimization Heuristic algorithms Statistics Sociology Benchmark testing Convergence Search problems constrained optimization problems hybrid methods imperialist competitive algorithm

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A novel filled function method for solving constrained optimization problems 4

A novel filled function method for solving constrained optim...

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4th IEEE Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)

作者： Li, Qing Yuan, Liu-yang Wuhan Univ Sci & Technol Coll Sci Wuhan 430072 Hubei Peoples R China

ISBN: (纸本)9781728143903

In this paper, a novel filled function method is proposed for solving constrained continuous global optimization problems without the coercive condition. The novel filled function, which is continuously differentiable and contains neither exponential term nor logarithmic term, is constructed at a point x* which is not necessarily a local optimal solution of the original problem. The implementation of the algorithm on several test problems is reported with numerical results.

关键词： The filled function method The local optimal solution constrained optimization problems Electronic and Automation Control

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An effective improved differential evolution algorithm to solve constrained optimization problems

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SOFT COMPUTING 2019年第7期23卷 2409-2427页

作者： Yu, Xiaobing Lu, Yiqun Wang, Xuming Luo, Xiang Cai, Mei Nanjing Univ Informat Sci & Technol Res Inst Hist Sci & Technol Nanjing 210044 Jiangsu Peoples R China Nanjing Univ Informat Sci & Technol Collaborat Innovat Ctr Forecast & Evaluat Meteoro Nanjing 210044 Jiangsu Peoples R China Nanjing Univ Informat Sci & Technol China Inst Manufacture Developing Nanjing 210044 Jiangsu Peoples R China Nanjing Univ Informat Sci & Technol Sch Econ & Management Nanjing 210044 Jiangsu Peoples R China Nanjing Univ Informat Sci & Technol Sch Elect & Informat Engn Nanjing 210044 Jiangsu Peoples R China

An effective extended differential evolution algorithm is proposed to deal with constrained optimization problems. The proposed algorithm adopts a new mechanism to cope with constrained problems by transforming the equality into inequality first. Then, two kinds of offspring generation approaches are applied to balance the diversity and the convergence speed of the population during evolution, and seven criteria are designed to compare feasible solution over infeasible solution. The performance of the novel algorithm is evaluated on a set of well-known constrained problems from CEC2006. The experimental results are quite competitive when comparing the proposed algorithm against state-of-the-art optimization algorithms.

关键词： Differential evolution Evolutionary algorithm Mutation strategy constrained optimization problems

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