Artificial fish swarm algorithm is one of the swarm intelligence algorithms which performs based on population and stochastic search contributed to solve optimization problems. This algorithm has been applied in vario...
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Artificial fish swarm algorithm is one of the swarm intelligence algorithms which performs based on population and stochastic search contributed to solve optimization problems. This algorithm has been applied in various applications e.g. data clustering, neural networks learning, nonlinear function optimization, etc. Several problems in real world are dynamic and uncertain, which could not be solved in a similar manner of static problems. In this paper, for the first time, a modified artificial fish swarm algorithm is proposed in consideration of dynamic environments optimization. The results of the proposed approach were evaluated using moving peak benchmarks, which are known as the best metric for evaluating dynamic environments, and also were compared with results of several state-of-the-art approaches. The experimental results show that the performance of the proposed method outperforms that of other algorithms in this domain.
In this paper a new electromagnetism-like mechanism is proposed for combinatorial optimization of capacitated vehicle routing problem. electromagnetism-like mechanism is a new metaheuristic method and inspired by the ...
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In this paper a new electromagnetism-like mechanism is proposed for combinatorial optimization of capacitated vehicle routing problem. electromagnetism-like mechanism is a new metaheuristic method and inspired by the attraction and repulsion mechanism of the electromagnetism theory. we propose a new electromagnetism-like mechanism that it includes a new distance measure between solutions and new effective process of attraction and repulsion. In order to analyze the proposed algorithm a comparison is done with existing algorithm for this problem. Computational results show that the proposed electromagnetism-like mechanism algorithm has a good performance for the considered problem.
Evolutionary methods are well-known techniques for solving nonlinear constrained optimization problems. Due to the exploration power of evolution-based optimizers, population usually converges to a region around globa...
Evolutionary methods are well-known techniques for solving nonlinear constrained optimization problems. Due to the exploration power of evolution-based optimizers, population usually converges to a region around global optimum after several generations. Although this convergence can be efficiently used to reduce search space, in most of the existing optimization methods, search is still continued over original space and considerable time is wasted for searching ineffective regions. This paper proposes a simple and general approach based on search space reduction to improve the exploitation power of the existing evolutionary methods without adding any significant computational complexity. After a number of generations when enough exploration is performed, search space is reduced to a small subspace around the best individual, and then search is continued over this reduced space. If the space reduction parameters (red_gen and red_factor) are adjusted properly, reduced space will include global optimum. The proposed scheme can help the existing evolutionary methods to find better near-optimal solutions in a shorter time. To demonstrate the power of the new approach, it is applied to a set of benchmark constrained optimization problems and the results are compared with a previous work in the literature.
Artificial music composition is one of the ever rising problems of computer science. Genetic Algorithm has been one of the most useful means in our hands to solve optimization problems. By use of precise assumptions a...
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Artificial music composition is one of the ever rising problems of computer science. Genetic Algorithm has been one of the most useful means in our hands to solve optimization problems. By use of precise assumptions and adequate fitness function it is possible to change the music composing into an optimization problem. This paper proposes a new genetic algorithm for composing music. Considering entropy of the notes distribution as a factor of fitness function and developing mutation and crossover functions based on harmonic rules and trying to keep the melodies intact during these processes would result in a musical piece pleasant to human ears and interesting for human mind. This algorithm does not have the constraints of the previous algorithms. Restraining mutation and crossover functions with a goal of producing melodies based on acceptable melodies composed by humans, this algorithm is not bound to any genre, instrument or melody. The experimental results of this approach show that it is near to the human composing and the results produced from it are more acceptable than the ones produced by its predecessors.
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