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A new class of meta-heuristics called SOMA (Self-Organizing Migrating Algorithm) was proposed in recent literature. SOMA works on a population of potential solutions called specimen and it is based on the self-organizing behavior of groups of individuals in a ??social environment??. This paper proposes a modified SOMA approach to solving the economic load dispatch problem of thermal generators with...
The reliability-redundancy allocation problem can be approached as a mixed-integer programming problem. It has been solved by using optimization techniques such as dynamic programming, integer programming, and mixed-integer nonlinear programming. On the other hand, a broad class of meta-heuristics has been developed for reliability-redundancy optimization. Recently, a new meta-heuristics called harmony...
Differential evolution (DE) is a powerful population-based algorithm of evolutionary computation field designed for solving global optimization problems. The potentialities of DE are its simple structure, easy use, convergence speed and robustness. However, the control parameters and learning strategies involved in DE are highly dependent on the problems under consideration. Choosing suitable parameter...
Particle swarm optimization (PSO) is a population-based stochastic optimization technique, originally developed by Eberhart and Kennedy, inspired by simulation of a social psychological metaphor instead of the survival of the fittest individual. In PSO, the system (swarm) is initialized with a population of random solutions (particles) and searches for optima using cognitive and social factors by...
Particle swarm optimization (PSO) is a population-based swarm intelligence algorithm driven by the simulation of a social psychological metaphor instead of the survival of the fittest individual. Based on the swarm intelligence theory, this paper discusses the use of PSO with a Quasi-Newton (QN) local search method. The PSO is used to produce good potential solutions, and the QN is used to fine-tune...
This work presents a new global optimization algorithm based on differential evolution (DE) method and DE combined with chaotic sequences (DEC) given by logistic map. In this paper, the optimal shape design of Loney's solenoids benchmark problem is carried out by DE and DEC algorithms. The results of DE and DEC approaches are also investigated and their performance compared with those reported in...
This work presents the use of particle swarm optimization (PSO) techniques with the particles' population space based on normative knowledge of cultural algorithms (CA). In this work, the optimal shape design of Loney's solenoids benchmark problem is carried out by PSO, PSO-CA, Gaussian PSO and Gaussian PSO-CA approaches
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