A novel differential evolution algorithm based on periodic intervention and systematic regulation mechanisms

IF 3.4 2区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Guanyu Yuan, Gaoji Sun, Libao Deng, Chunlei Li, Guoqing Yang
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引用次数: 0

Abstract

Differential evolution (DE) has attracted widespread attention due to its outstanding optimization performance and ease of operation, but it cannot avoid the dilemmas of premature convergence or stagnation when faced with complex optimization problems. To reduce the probability of such difficulties for DE, we sort out the factors that influence the balance between global exploration and local exploitation in the DE algorithm, and we design a novel DE variant (abbreviated as PISRDE) by integrating the corresponding influence factors through a periodic intervention mechanism and a systematic regulation mechanism. The periodic intervention mechanism divides the optimization operations of PISRDE into routine operation and intervention operation, and it balances global exploration and local exploitation at the macro level by executing the two operations alternately. The systematic regulation mechanism treats the involved optimization strategies and parameter settings as an organic system for targeted design, to balance global exploration and local exploitation at the meso or micro level. To evaluate and verify the optimization performance of PISRDE, we employ seven DE variants with excellent optimization performance to conduct comparison experiments on the IEEE CEC 2014 and IEEE CEC 2017 benchmarks. The comparison results indicate that PISRDE outperforms all competitors overall, and its relative advantage is even more significant when dealing with high-dimensional and complex optimization problems.

Schematic design philosophy of PISRDE

Abstract Image

Abstract Image

基于定期干预和系统调节机制的新型差分进化算法
摘要 差分进化(Differential Evolution,DE)以其优异的优化性能和易操作性受到广泛关注,但它在面对复杂优化问题时无法避免过早收敛或停滞不前的困境。为了降低渐进演化算法出现这种困境的概率,我们梳理了影响渐进演化算法中全局探索与局部开发平衡的因素,并通过周期性干预机制和系统性调节机制整合相应的影响因素,设计出一种新型渐进演化算法变体(简称 PISRDE)。定期干预机制将 PISRDE 的优化操作分为常规操作和干预操作,通过交替执行这两种操作,在宏观上平衡全局探索和局部开发。系统调控机制将所涉及的优化策略和参数设置作为一个有机系统进行有针对性的设计,在中观或微观层面平衡全局探索和局部开发。为了评估和验证 PISRDE 的优化性能,我们采用了七种优化性能优异的 DE 变体,在 IEEE CEC 2014 和 IEEE CEC 2017 基准上进行了对比实验。对比结果表明,PISRDE 的整体性能优于所有竞争对手,在处理高维和复杂优化问题时,其相对优势更为显著。 图式摘要 PISRDE 的设计理念示意图
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来源期刊
Applied Intelligence
Applied Intelligence 工程技术-计算机:人工智能
CiteScore
6.60
自引率
20.80%
发文量
1361
审稿时长
5.9 months
期刊介绍: With a focus on research in artificial intelligence and neural networks, this journal addresses issues involving solutions of real-life manufacturing, defense, management, government and industrial problems which are too complex to be solved through conventional approaches and require the simulation of intelligent thought processes, heuristics, applications of knowledge, and distributed and parallel processing. The integration of these multiple approaches in solving complex problems is of particular importance. The journal presents new and original research and technological developments, addressing real and complex issues applicable to difficult problems. It provides a medium for exchanging scientific research and technological achievements accomplished by the international community.
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