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Adding a diversity mechanism to a simple evolution strategy to solve constrained optimization problems

机译:在简单的进化策略中添加分集机制以解决约束优化问题

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In this paper, we propose the use of a simple evolution strategy (SES) (i.e., a (1 + /spl lambda/)-ES with self-adaptation that uses three tournament rules based on feasibility) coupled with a diversity mechanism to solve constrained optimization problems. The proposed mechanism is based on multiobjective optimization concepts taken from an approach called the niched-Pareto genetic algorithm (NPGA). The main advantage of the proposed approach is that it does not require the definition of any extra parameters, other than those required by an evolution strategy. The performance of the proposed approach is shown to be highly competitive with respect to other constraint-handling techniques representative of the state-of-the-art in the area when using a set of well-known benchmarks.
机译:在本文中,我们建议使用简单的进化策略(SES)(即具有(1 + / spl lambda /)-ES且具有自适应性的ES,使用基于可行性的三个锦标赛规则进行自适应)结合多样性机制来求解约束优化问题。所提出的机制基于多目标优化概念,该概念取自一种称为“尼基-帕累托遗传算法”(NPGA)的方法。所提出的方法的主要优点是,除了进化策略所需的参数外,它不需要定义任何额外的参数。当使用一组众所周知的基准时,相对于代表该领域最新技术的其他约束处理技术,所提出的方法的性能显示出极高的竞争力。

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