In order to testing the performance of our approach BEPSO recently proposed on the different kind of problem. We tested in this paper to solving the constrainedengineeringoptimization problem with BEPSO algorithm. W...
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(纸本)9781450376556
In order to testing the performance of our approach BEPSO recently proposed on the different kind of problem. We tested in this paper to solving the constrainedengineeringoptimization problem with BEPSO algorithm. We compared the result obtained with BEPSO on two problems: Pressure vessel, and compression spring; with those published and obtained with other algorithm; the results have shown the superiority of our approach compared to the other approaches both in terms of quality of solutions and convergence speed.
This paper proposes a multi-strategy seeker optimization algorithm (MSSOA) for optimizationconstrainedengineeringproblems. In this paper, three strategies were adopted to improve the poor searching capability of th...
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This paper proposes a multi-strategy seeker optimization algorithm (MSSOA) for optimizationconstrainedengineeringproblems. In this paper, three strategies were adopted to improve the poor searching capability of the seeker optimization algorithm (SOA). The first strategy was triple black hole system capture to solve the local optima issue. The second and third strategies were the multi-dimensional random interference and the precocious interference to balance the exploration and exploitation processes. These three strategies are proposed to improve respectively the SOA algorithm, and compared with the three strategies to improve together the SOA algorithm for optimizing 15 benchmark functions;the way these three strategies work together is called multi-strategy;and the efficiency of the multi-strategy is illustrated the numerical optimizing results and the convergence curves, population's positions with iterations and the search history of the benchmark functions. The proposed multi-strategy method achieved better performance in optimizing of the benchmark functions compared to other six optimization methods. The numerical and experimental results analysis were observed with respect to the optimal solution curve, the convergence curve of the fitness function, the ANOVA tests, the calculation complexity of the algorithm, the running time of the algorithm routine, the exploration and exploitation capability, the Wilcoxon's rank-sum test, the performance profile of algorithm. The results showed that the proposed multi-strategy method was efficient in the benchmark functions. The proposed multi-strategy method also achieved better performance in optimizing of the engineeringproblems and provided better solutions compared to other six optimization methods.
This research proposes a reinforced salp swarm algorithm (SSA) variant with an ensemble mutation strategy and a restart mechanism, which is named CMSRSSSA for short, to enhance exploration and exploitation capacity of...
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This research proposes a reinforced salp swarm algorithm (SSA) variant with an ensemble mutation strategy and a restart mechanism, which is named CMSRSSSA for short, to enhance exploration and exploitation capacity of SSA and conquer the restriction of a single search mechanism of the SSA in tackling continuous optimizationproblems. In this variant, an ensemble/composite mutation strategy (CMS) can boost the exploitation and exploration trends of SSA, as well as restart strategy (RS) is capable of assisting salps in getting away from local optimum. To investigate the performance of the proposed optimizer, firstly, IEEE CEC2017 benchmark problems are used to estimate the capability of the presented CMSRSSSA in solving continuous optimizationproblems in comparison to other advanced algorithms;furthermore, IEEE CEC2011 real-world benchmark problems and constrained engineering optimization problems are also utilized to assess the performance of CMSRSSSA for practical ideas. Experimental and statistical results reveal that the CMSRSSSA outperforms all the competitors, including winners of the related IEEE CEC competition;therefore, it will be able to be treated as a promising method in resolving both constrained and unconstrainedoptimizationproblems. For post-publication supports and guides on the idea of the paper, please be in touch with the hosting website: http://***.
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