Strategy dynamics particle swarm optimizer
Strategy dynamics particle swarm optimizer
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DOI：
10.1016/j.ins.2021.10.028
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发表时间：
2021-10
期刊：
影响因子：
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通讯作者：
Ziang Liu;T. Nishi
中科院分区：
文献类型：
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作者：
Ziang Liu;T. Nishi
本文提出了一种基于策略动力学的粒子群优化方法（SDPSO）来解决单目标优化问题。SDPSO由四种粒子群搜索策略组成。引入了进化博弈论来控制种群状态。在进化博弈论中，通过参与者之间的相互作用，更好的策略最终会在参与者中占主导地位。将这一思想推广到粒子群算法中，提出了粒子群算法的选择机制和突变机制。通过使用选择机制，高收益策略的采用概率会增加。突变机制可以检验现有策略在进化压力下的稳定性。在CEC 2014测试套件上比较了SDPSO与14种算法的性能。结果表明，SDPSO排序最高。SDPSO应用于解决现实问题。与4种算法相比，SDPSO能找到最好的均值。研究结果表明，基于进化博弈论的框架能够自适应控制种群状态。本研究提出了进化博弈论在群体智能设计中的新应用，有助于更好地理解进化博弈论在优化方法中的实用性。SDPSO的源代码可从https://github.com/zi-ang-liu/SDPSO获得。
This paper proposes a particle swarm optimization with strategy dynamics (SDPSO) to solve single-objective optimization problems. SDPSO consists of four PSO search strategies. Evolutionary game theory is introduced to control the population state. In evolutionary game theory, through the interaction between players, better strategies will eventually dominate among the players. By extending this idea to PSO, a selection mechanism and a mutation mechanism are proposed. By using the selection mechanism, the adoption probability of the high payoff strategies will increase. The mutation mechanism can examine the stability of the incumbent strategy to evolutionary pressures. The performance of SDPSO is compared with 14 algorithms on the CEC 2014 test suite. The results show that SDPSO has the highest rank. SDPSO is applied to solve a real-world problem. SDPSO can find the best mean results comparing with 4 algorithms. The findings show that the proposed evolutionary game theory-based framework can adaptively control the population state. This study proposes a new application of evolutionary game theory to the design of swarm intelligence and contributes to a better understanding of the usefulness of the evolutionary game theory in the optimization method. The source codes of SDPSO are available at https://github.com/zi-ang-liu/SDPSO.