混合约束多峰优化问题的一个协同共轭进退粒子群算法

为解决混合（等式和不等式）约束的多峰优化问题（MOPs），本文在粒子群算法框架下提出了粒子优度比较准则和局部协同与共轭进退寻优两种迭代进化策略．优度比较准则在适应度和约束违反度的双重限制下指导粒子高效地执行进化策略，局部协同策略可使粒子能通过局部抱团收敛到多个全局最优解，而共轭进退寻优策略则提升了寻优的速度和精度．基于优度比较准则与两种进化策略的有效结合，本文设计了一个协同共轭进退粒子群（CCARPSO）算法，以充分融合粒子群算法的全局搜索能力和共轭进退法的局部快速寻优能力．数值仿真表明，该算法能有效解决复杂约束MOPs和非线性方程组的多根问题，在广义Logistic分布的参数估计中有全局优化能力和较高的计算精度．

A cooperative conjugate advance-retreat particle swarm optimization algorithm for hybrid constrained multimodal optimization problems

This paper aims at the multimodal optimization problems (MOPs) with equality and inequality constraints. A new algorithm is proposed following the particle swarm optimization idea. This algorithm consist of a superiority comparison criterion and two iterative evolutionary strategies. The superiority comparison criterion guides the particles on how to evolute according to the constructed constraint violation degree and the fitness ( i.e., the objective function value). The local cooperation strategy ensures that all particles can converge to multiple global optimal solutions through local clustering. The conjugate advance-retreat optimization strategy improves the speed and precision of optimization. The new algorithm, named Cooperative Conjugate Advance-Retreat Particle Swarm Optimization (CCARPSO) algorithm, integrates the global searching ability of the PSO and the local fast optimization capability of the conjugate advance-retreat method. In the numerical simulations, the algorithm can effectively solves MOPs with complex constraints and nonlinear equations with multiple solutions, and has high global optimization ability and calculation accuracy in estimating parameters of the generalized Logistic distribution.