An Improved Adaptive Niche Differential Evolution Algorithm

Differential evolution (DE) algorithm is a random search algorithm by referring to the natural genetic and natural selection mechanism of the biological world and it is used to process the complicated non-linear problems which are difficult to be solved by traditional computational methods. However, subject to its own mechanism and single structure, the basic DE algorithm is easy to get trapped into local optimum and it is difficult to handle high-dimensional and complicated optimization problems. In order to enhance the search performance of the DE algorithm, this paper uses the idea of niche, decomposes the entire population into several niches according to the fitness, perform population selection by integrating the optimum reservation strategy to realize the optimal selection of niche, adjusts the fitness of the individual of the population, designs the adaptive crossover and mutation operators to make the crossover and mutation probabilities change with the individual fitness and enhances the ability of DE algorithm to jump out of the local optimal solution. The experiment result of benchmark function shows that the method of this paper can maintain solution diversity, effectively avoid premature convergence and enhance the global search ability of DE algorithm

Population-based Algorithm Portfolios with automated constituent algorithms selection

AbstractPopulation-based Algorithm Portfolios (PAP) is an appealing framework for integrating different Evolutionary Algorithms (EAs) to solve challenging numerical optimization problems. Particularly, PAP has shown significant advantages to single EAs when a number of problems need to be solved simultaneously. Previous investigation on PAP reveals that choosing appropriate constituent algorithms is crucial to the success of PAP. However, no method has been developed for this purpose. In this paper, an extended version of PAP, namely PAP based on Estimated Performance Matrix (EPM-PAP) is proposed. EPM-PAP is equipped with a novel constituent algorithms selection module, which is based on the EPM of each candidate EAs. Empirical studies demonstrate that the EPM-based selection method can successfully identify appropriate constituent EAs, and thus EPM-PAP outperformed all single EAs considered in this work

Performance assessment of meta-heuristics for composite layup optimisation

Peer reviewedPostprin

Population-based Algorithm Portfolios with automated constituent algorithms selection

AbstractPopulation-based Algorithm Portfolios (PAP) is an appealing framework for integrating different Evolutionary Algorithms (EAs) to solve challenging numerical optimization problems. Particularly, PAP has shown significant advantages to single EAs when a number of problems need to be solved simultaneously. Previous investigation on PAP reveals that choosing appropriate constituent algorithms is crucial to the success of PAP. However, no method has been developed for this purpose. In this paper, an extended version of PAP, namely PAP based on Estimated Performance Matrix (EPM-PAP) is proposed. EPM-PAP is equipped with a novel constituent algorithms selection module, which is based on the EPM of each candidate EAs. Empirical studies demonstrate that the EPM-based selection method can successfully identify appropriate constituent EAs, and thus EPM-PAP outperformed all single EAs considered in this work

Parallel population-based algorithm portfolios::An empirical study

Although many algorithms have been proposed, no single algorithm is better than others on all types of problems. Therefore, the search characteristics of different algorithms that show complementary behavior can be combined through portfolio structures to improve the performance on a wider set of problems. In this work, a portfolio of the Artificial Bee Colony, Differential Evolution and Particle Swarm Optimization algorithms was constructed and the first parallel implementation of the population-based algorithm portfolio was carried out by means of a Message Passing Interface environment. The parallel implementation of an algorithm or a portfolio can be performed by different models such as master-slave, coarse-grained or a hybrid of both, as used in this study. Hence, the efficiency and running time of various parallel implementations with different parameter values and combinations were investigated on benchmark problems. The performance of the parallel portfolio was compared to those of the single constituent algorithms. The results showed that the proposed models reduced the running time and the portfolio delivered a robust performance compared to each constituent algorithm. It is observed that the speedup gained over the sequential counterpart changed significantly depending on the structure of the portfolio. The portfolio is also applied to a training of neural networks which has been used for time series prediction. Result demonstrate that, portfolio is able to produce good prediction accuracy. (C) 2017 Elsevier B.V. All rights reserved