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Artificial Bee Colony (ABC) algorithm is one of the most recent swarm intelligence algorithms which used for problem optimization. This paper presents a Hybrid Artificial Bee Colony algorithm (HABC), in which the crossover operator of Genetic Algorithm is introduced, to improve the canonical ABC in solving complex optimization problems. The variation of the algorithm is presented and seven benchmark...
Real-coded genetic algorithms (RCGAs) have been effectively used to solve constrained optimization problems (COPs). However, the crossover operators do not have mechanisms to handle constraints and there is no guarantee that if the parents satisfy some constraints the offspring will satisfy them as well. Degree preserving based crossover operators are proposed to increase the probability of constructing...
In this paper, an improved genetic algorithm based on polygymy which means one father and many mothers is proposed. The population is divided into several sub-populations, and each sub-population is composed of a father, some mothers and some bachelors. Crossover is occurred between the father and the mothers. Mutation is occurred only among the bachelors. The function optimization results show that...
Various communication algorithms have been proposed on a variety of network architectures. However, an efficient algorithm on an efficient architecture is always the focus point. We have proposed all to all broadcast (AAB) algorithm on multi mesh trees using parallel random access machine (PRAM), which combines the topological properties of multi-mesh and mesh of trees architecture. The algorithm...
In this paper we describe a method to evolve biologically inspired motion detection systems utilizing artificial neural networks (ANN's). Previously, the evolution of neural networks has focused on feed-forward neural networks or networks with predefined architectures. The purpose of this paper is to present a novel method for evolving neural networks with no predefined architectures to solve various...
In this paper, a new genetic algorithm is developed based on a pre-existing implementation. The new algorithm requires less human interaction through the use of dynamically selected weight and acceptance probability parameters. The algorithm is implemented and tested using six benchmark functions. Results show that the new algorithm significantly outperforms other genetic algorithms in less time and...
Proposing a new algorithm which is simple but effective. Using characteristic of biological evolution and common sense to design the selection operator, improve the variation method of the crossover probability and the mutation probability. Numerical experiments show that the new algorithm is more effective than the comparative algorithm in realizing the high convergence speed, convergence precision,...
In the first place, an improvement was made on crossover and mutation of adaptive genetic algorithm (AGA) to let the crossover probability and mutation probability adapt nonlinearly. Then a comparison was made between Improved adaptive genetic algorithm (IAGA) and adaptive genetic algorithm (AGA) in segmentation time and adaptive function curve. The results indicated that IAGA can give attention to...
Standard genetic algorithms have the defects of pre-maturity and stagnation when applied in optimizing problems. In order to avoid the shortcomings, an adaptive niche genetic algorithm (ANGA) is proposed. The Elitist strategy is utilized to ensure the stable convergence, niche ideology is used to maintain diversity of evolution population, and the adaptive crossover rate and mutation probability are...
In this paper, based on adaptive genetic algorithm and grey relation degree, a new method for solving multi-objective problem is put forward. First, the best solution of every objective in the middle of the multi-objective is solved and they are looked on as referenced vector. Second, the grey relation degree between every individual and the referenced vector is solved and the grey relation degree...
By taking advantage of high performance computing technology, a dynamic parallel genetic algorithm (DPGA) has been developed in IHPC for DSM/VDSM chip planning. We present a new speckle model for searching optimal solutions in multi-dimensional space. Some new principles of peristalsis operators have been developed for density searching purposes. Dynamic control strategies in solution evaluation,...
The paper presents a distributed genetic algorithm implementation for obtaining good quality consistent results for different ordering problems. Most importantly, the solution found by the proposed Distributed GA is not only of high quality but also robust and does not require fine tuning of the probabilities of crossover and mutation. In addition, implementation of the Distributed GA is simple and...
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