The pareto optimal control of inverter based on multi-objective immune algorithm

Yuan, Jiaxin; Xue, Gang; Zhao, Zhijia; Chen, Baichao

doi:10.1109/icpe.2015.7167969

Cited by 2 publications

(3 citation statements)

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“…In this section, we compared the coupling algorithm with the state-of-the-art evolutionary algorithms NSGA-II [22], SPEA2 [25], PNIA [26], MOEA/D [23]. The parameter values of these algorithms are listed in Table 2.…”

Section: Comparison With State-of-the-art Algorithmmentioning

confidence: 99%

“…The parameter values of these algorithms are listed in Table 2. For more details about these algorithms, please refer to the literature [25,26]. In our research work, each work compares MGSO-BFO with NSGA-II, SPEA2 PNIA, and MOEAD the typical simulation results are shown in Table A1.…”

Section: Comparison With State-of-the-art Algorithmmentioning

confidence: 99%

“…Zitzler et al proposed SPEA2 [25], which tried to mix adaptive value allocation, archive truncation, and the density selection strategies. Gong et al [26] use of the strength Pareto genetic algorithm (GA) with immunity as a tool to solve multi-objective optimization problems in the maintenance of aircraft equipment and propose the PNIA algorithm.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Coupling Algorithm Based on Glowworm Swarm Optimization and Bacterial Foraging Algorithm for Solving Multi-Objective Optimization Problems

Wang

Cui

2019

Algorithms

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In the real word, optimization problems in multi-objective optimization (MOP) and dynamic optimization can be seen everywhere. During the last decade, among various swarm intelligence algorithms for multi-objective optimization problems, glowworm swarm optimization (GSO) and bacterial foraging algorithm (BFO) have attracted increasing attention from scholars. Although many scholars have proposed improvement strategies for GSO and BFO to keep a good balance between convergence and diversity, there are still many problems to be solved carefully. In this paper, a new coupling algorithm based on GSO and BFO (MGSOBFO) is proposed for solving dynamic multi-objective optimization problems (dMOP). MGSOBFO is proposed to achieve a good balance between exploration and exploitation by dividing into two parts. Part I is in charge of exploitation by GSO and Part II is in charge of exploration by BFO. At the same time, the simulation binary crossover (SBX) and polynomial mutation are introduced into the MGSOBFO to enhance the convergence and diversity ability of the algorithm. In order to show the excellent performance of the algorithm, we experimentally compare MGSOBFO with three algorithms on the benchmark function. The results suggests that such a coupling algorithm has good performance and outperforms other algorithms which deal with dMOP.

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Section: Comparison With State-of-the-art Algorithmmentioning

confidence: 99%

Section: Comparison With State-of-the-art Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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