Increasing of net energy storage (Q net) and discharge time of phase change material (t PCM), simultaneously, are important purpose in the design of solar systems. In the present paper, Multi-Objective (MO) based on hybrid of Particle Swarm Optimization (PSO) and multiple crossover and mutation operator is used for Pareto based optimization of solar systems. The conflicting objectives are Q net...

In present study, a three-step multi-objective optimization algorithm of cyclone separators is catered for the design objectives. First, the pressure drop (Dp) and collection efficiency (h) in a set of cyclone separators are numerically evaluated. Secondly, two meta models based on the evolved Group Method of Data Handling (GMDH) type neural networks are regarded to model the Dp and h as the re...

The performance of the distributed coherent aperture radar (DCAR) is heavily influenced by antenna positions. Therefore, an position optimization method proposed based on adaptive genetic algorithm with a self-supervised differential operator. In method, positions are firstly coded as chromosomes population multiple constraints, and reciprocal peak side lobe level (PSLL) beam pattern calculated...

Genetic Algorithm (GAs) is used to solve optimization problems. It is depended on the selection operator, crossover and mutation rates. In this paper Roulette Wheel Selection (RWS) operator with different crossover and mutation probabilities, is used to solve well known optimization problem Traveling Salesmen Problem (TSP). We have compared the results of RWS with another selection method Stoch...

⎯The convergence of genetic algorithm is mainly determined by its core operation crossover operation. When the objective function is a multiple hump function, traditional genetic algorithms are easily trapped into local optimum, which is called premature convergence. In this paper, we propose a new genetic algorithm with improved arithmetic crossover operation based on gradient method. This cro...

Genetic algorithms are optimisation algorithms and mimic the natural process of evolution. Important operators used in genetic algorithms are selection, crossover and mutation. Selection operator is used to select the individuals from a population to create a mating pool which will participate in reproduction process. Crossover and mutation operators are used to introduce diversity in the popul...

In this article, a novel hybrid genetic algorithm is proposed. The selection operator, crossover operator andmutation operator of the genetic algorithm have effectively been improved according to features of Sudoku puzzles. The improved selection operator has impaired the similarity of the selected chromosome and optimal chromosome in the current population such that the chromosome with more ab...

Inspired by evolutionary game theory, this paper modifies previous mixed strategy framework, adding a new mutation operator and extending to crossover operation, and proposes co-evolutionary algorithms based on mixed crossover and/or mutation strategy. The mixed mutation strategy set consists of Gaussian, Cauchy, Levy, single point and differential mutation operators; the mixed crossover strate...