Crossover-first differential evolution for improved global optimization in non-uniform search landscapes

The differential evolution (DE) algorithm is currently one of the most widely used evolutionary-based optimizers for global optimization due to its simplicity, robustness and efficiency. The DE algorithm generates new candidate solutions by first conducting the mutation operation which is then followed by the crossover operation. This order of genetic operation contrasts with other evolutionary algorithms where crossover typically precedes mutation. In this study, we investigate the effects of conducting crossover first and then followed by mutation in DE which we named as crossover-first differential evolution (XDE). In order to test this simple and straightforward modification to the DE algorithm, we compared its performance against the original DE algorithm using the CEC2005 global optimization’s set of 25 continuous optimization test problems. The statistical results indicate that the average performance of XDE is better than the original DE and three other well-known global optimizers. This straightforward reversal in the order of the genetic operations in DE can indeed improve its performance, in particular when attempting to solve complex search spaces with highly non-uniform landscapes

A new evolutionary search strategy for global optimization of high-dimensional problems

Global optimization of high-dimensional problems in practical applications remains a major challenge to the research community of evolutionary computation. The weakness of randomization-based evolutionary algorithms in searching high-dimensional spaces is demonstrated in this paper. A new strategy, SP-UCI is developed to treat complexity caused by high dimensionalities. This strategy features a slope-based searching kernel and a scheme of maintaining the particle population's capability of searching over the full search space. Examinations of this strategy on a suite of sophisticated composition benchmark functions demonstrate that SP-UCI surpasses two popular algorithms, particle swarm optimizer (PSO) and differential evolution (DE), on high-dimensional problems. Experimental results also corroborate the argument that, in high-dimensional optimization, only problems with well-formative fitness landscapes are solvable, and slope-based schemes are preferable to randomization-based ones. © 2011 Elsevier Inc. All rights reserved

Analyzing the Scalability Performance of Crossover-First and Self-Adaptive Differential Evolution Algorithms for Complex Numerical Optimization

Two Crossover-first Differential Evolution (XDE) algorithms as well as four self-adaptive DE algorithms are compared in this study in terms of their optimization accuracy for solving a set of 15 complex, non-linear numerical optimization functions across 4 different dimensions of 10, 30, 50 and 100 optimization variables. XDE is a crossover-first variant of the original DE algorithm where XjDE is the crossover-first variant of the self-adaptive jDE algorithm. The original DE representing a fixed parameter strategy is tested against four self-adaptive algorithms, namely the DESACR, DESACRF, SDE and jDE algorithms. Although XDE is able to outperform XjDE in all 15 test problems for the lowest dimensional benchmark test setting of 10 variables, the crossover-first approach in XjDE is able to improve its performance and obtained better results over XDE in some of the test problems for the higher-dimensional benchmark test settings of 30, 50 and 100 variables. As such, this shows that there is some merit in adopting the crossover-first approach into the self-adaptive XjDE algorithm since the CR and F parameters are automatically adjusted and optimized by the algorithm itself as compared to the fixed CR and F in XDE which has to be manually tuned by hand. The results also show that different self-adaptive parameter tuning schemes have significantly different effects on the performance of DE as the number of optimization dimensions increases

Generalized Hybrid Evolutionary Algorithm Framework with a Mutation Operator Requiring no Adaptation

This paper presents a generalized hybrid evolutionary optimization structure that not only combines both nondeterministic and deterministic algorithms on their individual merits and distinct advantages, but also offers behaviors of the three originating classes of evolutionary algorithms (EAs). In addition, a robust mutation operator is developed in place of the necessity of mutation adaptation, based on the mutation properties of binary-coded individuals in a genetic algorithm. The behaviour of this mutation operator is examined in full and its performance is compared with adaptive mutations. The results show that the new mutation operator outperforms adaptive mutation operators while reducing complications of extra adaptive parameters in an EA representation