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As a relatively new population-based technique for the continuous optimization problems, artificial bee colony algorithm (ABC) has verified its superiority and robustness over traditional evolutionary algorithms. However, regarding the wide variety of ABC extensions, the use of search experience produced during the search progress has been relatively unexplored to improve the performance. This results...
In this paper, a strength Pareto evolutionary algorithm (SPEA) based approach is proposed for designing an integrated fuzzy guidance law which consists of three fuzzy controllers. Each of these controllers is activated in a region of the interception. The distribution of the membership functions and the rules are obtained by solving a nonlinear constrained multi-objectives optimization problem where...
Modern industries, e.g., semiconductor packaging, imposes increasing stringent requirement on equipment with very high acceleration and high precision. Traditionally, arm linkage and drive mechanism are first designed followed by control design. The integrated design method is proposed as a preferable technique of the traditional one. In this paper, a general framework of the integrated design method...
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 novel modified differential evolution (MDE) algorithm that contains simplex acceleration operator and chaotic migration operator is proposed for feed rate optimization of fed-batch fermentation process. The simplex acceleration operator improves the speed of getting global optimum, and the chaotic migration operator keeps the individualspsila diversity in the population to overcome prematurity....
The particle swarm optimization algorithm (PSO) has successfully been applied to many engineering optimization problems. However, most of the existing improved PSO algorithms work well only for small-scale problems. In this new self-adaptive PSO, a special function, which is defined in terms of the particle fitness and swarm size, is introduced to adjust the inertia weight adaptively. In a given generation,...
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