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A new method of optimization recently made popular in the evolutionary computation (EC) community is introduced and applied to several electromagnetics design problems. First, a functional overview of the covariance matrix adaptation evolutionary strategy (CMA-ES) is provided. Then, CMA-ES is critiqued alongside a conventional particle swarm optimization (PSO) algorithm via the design of a wideband...
A parameter automation strategy for particle swarm optimization (PSO) is introduced to enhance the performance to solve high dimensions objects. Initially, to maintain the diversities of the population, the concept of ??individual coefficients?? (IC) is employed, where each particle has the individual inertia weight and social acceleration coefficient. From the basis of IC, The ??individual coefficients??...
The particle swarm optimization algorithm (PSO) has successfully been applied to many engineering optimization problems. However, the most of existing improved PSO algorithms work well only for small-scale problems on low-dimensional space. In this new self-adaptive PSO, a special function, which is defined in terms of the particle fitness, swarm size and the dimension size of solution space, is introduced...
A new particle swarm optimization characterized by sensation is presented to improve the limited capability of regular particle swarm optimization in exploiting history experience (iwPSO). It guides individuals to behave reasonably with the capability of self-adaptation in activities of self-cognition according to the sensation model. Considering the complexity of a swarm intelligent system at the...
A self-adaptive mutation-particle swarm optimization algorithm is proposed in this paper. In this algorithm, firstly, to avoid the randomness of updating particle velocity, a modified velocity updating formula of the particle which varies with convergence factor and the diffusion factor is proposed by adaptive inertia weight. Secondly, the introduction of stochastic mutation operators enhances the...
As a new version of particle swarm optimization (PSO), PID-controlled PSO introduces the concept of controller into the algorithm structure. However, with the introduction of PID controller, three additional parameters are incorporated into the algorithm. Thus, how to provide a proper selection of these parameters is an important problem to affect the algorithm efficiency. In this paper, the relationships...
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