This paper proposes a novel enhancement for competitive swarm optimizer (CSO) by mutating loser particles (agents) from the swarm to increase the swarm diversity and improve space exploration capability, namely competitive swarm optimizer with mutated agents (CSO-MA). The selection mechanism is carried out so that it does not retard the search if agents are exploring in promising areas. Simulation results show that CSO-MA has a better exploration–exploitation balance than CSO and generally outperforms CSO, which is one of the state-of-the-art metaheuristic algorithms for optimization. We show additionally that it also generally outperforms swarm based types of algorithms and an exemplary and popular non-swarm based algorithm called Cuckoo search, without requiring a lot more CPU time. We apply CSO-MA to find a c-optimal approximate design for a high-dimensional optimal design problem when other swarm algorithms were not able to. As applications, we use the CSO-MA to search various optimal designs for a series of high-dimensional statistical models. The proposed CSO-MA algorithm is a general-purpose optimizing tool and can be directly amended to find other types of optimal designs for nonlinear models, including optimal exact designs under a convex or non-convex criterion.

中文翻译：

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具有突变代理的竞争性群优化器，用于为具有多个相互作用因素的非线性回归模型寻找最佳设计

本文提出了一种新的竞争性群体优化器（CSO）的增强方法，即通过变异群体中的失败者粒子（代理）来增加群体多样性并提高空间探索能力，即具有变异代理的竞争性群体优化器（CSO-MA）。执行选择机制以便在代理在有前景的领域进行探索时不会阻碍搜索。仿真结果表明，CSO-MA 比 CSO 具有更好的探索-利用平衡，并且总体上优于 CSO，这是最先进的元启发式优化算法之一。我们还表明，它通常也优于基于群的算法类型和一个示例性且流行的基于非群的算法，称为 Cuckoo 搜索，而不需要更多的 CPU 时间。我们应用 CSO-MA 来找到一个c-当其他群算法无法实现时，高维优化设计问题的最优近似设计。作为应用，我们使用 CSO-MA 来搜索一系列高维统计模型的各种优化设计。所提出的 CSO-MA 算法是一种通用优化工具，可以直接修改以找到非线性模型的其他类型的优化设计，包括凸或非凸准则下的最佳精确设计。