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...

The present study is an attempt to propose a mutation-based real-coded genetic algorithm (MBRCGA) for sizing and layout optimization of planar and spatial truss structures. The Gaussian mutation operator is used to create the reproduction operators. An adaptive tournament selection mechanism in combination with adaptive Gaussian mutation operators are proposed to achieve an effective search in ...

Biogeography-Based Optimization (BBO) has recently gained interest of researchers due to its simplicity in implementation, efficiency and existence of very few parameters. The BBO algorithm is a new type of optimization technique based on biogeography concept. This population-based algorithm uses the idea of the migration strategy of animals or other species for solving optimization problems. t...

Evolutionary Algorithms (EAs) have recently been successfully applied to numerical optimization problems. A major obstacle in the application of EAs has been the relatively slow convergence rate. This becomes more pronounced when the functions to be optimized become complex and numerically intensive. In this paper five different methods of speeding up EA convergence are reviewed. These include ...

In this paper, we propose a genetic algorithm, called GenSPN, for finding highly probable differential characteristics of substitution permutation networks (SPNs). A special fitness function and a heuristic mutation operator have been used to improve the overall performance of the algorithm. We report our results of applying GenSPN for finding highly probable differential characteristics of Ser...

Since the advent of evolution strategies many attempts have been done to improve the mutation operator [7]. The first enhancement could be seen in the introduction of a flexible step size. As a logical consequence later n step sizes, one per variable, were used. Highest flexibility is achieved with the correlated mutation but also quadratic growth of the number of strategy parameters [8]. A new...

In this paper, we present a new mutation operator, Hybrid Mutation (HPRM), for a genetic algorithm that generates high quality solutions to the Traveling Salesman Problem (TSP). The Hybrid Mutation operator constructs an offspring from a pair of parents by hybridizing two mutation operators, PSM and RSM. The efficiency of the HPRM is compared as against some existing mutation operators; namely,...

Differential Evolution algorithm is a new competitive heuristic optimization algorithm in the continuous field. The operators in the original Differential Evolution are simple; however, these operators make it impossible to use the Differential Evolution in the binary space directly. Based on the analysis of problems led by the mutation operator of the original Differential Evolution in the bin...

An annealing mutation operator in the genetic algorithms (GA) for RNA folding on a MasPar MP-2 has been designed. The mutation probability descends along a hyperbola with respect to the size of the secondary structure, hence the total number of mutations at each generation drops linearly. Especially for long sequences with thousands of nucleotides as opposed to hundreds of nucleotides, the new ...