A Novel Memetic Framework for Enhancing Differential Evolution Algorithms via Combination With Alopex Local Search

Differential evolution (DE) represents a class of population-based optimization techniques that uses differences of vectors to search for optimal solutions in the search space. However, promising solutions/regions are not adequately exploited by a traditional DE algorithm. Memetic computing has been popular in recent years to enhance the exploitation of global algorithms via incorporation of local search. This paper proposes a new memetic framework to enhance DE algorithms using Alopex Local Search (MFDEALS). The novelty of the proposed MFDEALS framework lies in that the behavior of exploitation (by Alopex local search) can be controlled based on the DE global exploration status (population diversity and search stage). Additionally, an adaptive parameter inside the Alopex local search enables smooth transition of its behavior from exploratory to exploitative during the search process. A study of the important components of MFDEALS shows that there is a synergy between them. MFDEALS has been integrated with both the canonical DE method and the adaptive DE algorithm L-SHADE, leading to the MDEALS and ML-SHADEALS algorithms, respectively. Both algorithms were tested on the benchmark functions from the IEEE CEC’2014 Conference. The experiment results show that Memetic Differential Evolution with Alopex Local Search (MDEALS) not only improves the original DE algorithm but also outperforms other memetic DE algorithms by obtaining better quality solutions. Further, the comparison between ML-SHADEALS and L-SHADE demonstrates that applying the MFDEALS framework with Alopex local search can significantly enhance the performance of L-SHADEThis research was supported by grants from both Swedish Research Council (project number 2016-05431) and Spanish Ministry of Science TIN2016- 8113-R

Memetic Differential Evolution with an Improved Contraction Criterion

Memetic algorithms with an appropriate trade-off between the exploration and exploitation can obtain very good results in continuous optimization. In this paper, we present an improved memetic differential evolution algorithm for solving global optimization problems. The proposed approach, called memetic DE (MDE), hybridizes differential evolution (DE) with a local search (LS) operator and periodic reinitialization to balance the exploration and exploitation. A new contraction criterion, which is based on the improved maximum distance in objective space, is proposed to decide when the local search starts. The proposed algorithm is compared with six well-known evolutionary algorithms on twenty-one benchmark functions, and the experimental results are analyzed with two kinds of nonparametric statistical tests. Moreover, sensitivity analyses for parameters in MDE are also made. Experimental results have demonstrated the competitive performance of the proposed method with respect to the six compared algorithms

A Multi-Objective Memetic Optimization Method for Power Network Cascading Failures Protection

International audienceReliable and safe power grid operation requires the anticipation of cascading failures and the establishment of appropriate protection plans for their management. In this paper, we address this latter problem by line switching and propose a multi-objective memetic algorithm (MOMA), which combines the binary differential evolution algorithm with the non-dominated sorting mechanism and the Lamarckian local search. The 380 kV Italian power transmission network is used as a realistic test case

Mixed-Integer Constrained Optimization Based on Memetic Algorithm

Evolutionary algorithms (EAs) are population-based global search methods. They have been successfully applied tomany complex optimization problems. However, EAs are frequently incapable of finding a convergence solution indefault of local search mechanisms. Memetic Algorithms (MAs) are hybrid EAs that combine genetic operators withlocal search methods. With global exploration and local exploitation in search space, MAs are capable of obtainingmore high-quality solutions. On the other hand, mixed-integer hybrid differential evolution (MIHDE), as an EA-basedsearch algorithm, has been successfully applied to many mixed-integer optimization problems. In this paper, amemetic algorithm based on MIHDE is developed for solving mixed-integer optimization problems. However, most ofreal-world mixed-integer optimization problems frequently consist of equality and/or inequality constraints. In order toeffectively handle constraints, an evolutionary Lagrange method based on memetic algorithm is developed to solvethe mixed-integer constrained optimization problems. The proposed algorithm is implemented and tested on twobenchmark mixed-integer constrained optimization problems. Experimental results show that the proposed algorithmcan find better optimal solutions compared with some other search algorithms. Therefore, it implies that the proposedmemetic algorithm is a good approach to mixed-integer optimization problems

A MOS-based Dynamic Memetic Differential Evolution Algorithm for Continuous Optimization: A Scalability Test

Continuous optimization is one of the areas with more activity in the field of heuristic optimization. Many algorithms have been proposed and compared on several benchmarks of functions, with different performance depending on the problems. For this reason, the combination of different search strategies seems desirable to obtain the best performance of each of these approaches. This contribution explores the use of a hybrid memetic algorithm based on the multiple offspring framework. The proposed algorithm combines the explorative/exploitative strength of two heuristic search methods that separately obtain very competitive results. This algorithm has been tested with the benchmark problems and conditions defined for the special issue of the Soft Computing Journal on Scalability of Evolutionary Algorithms and other Metaheuristics for Large Scale Continuous Optimization Problems. The proposed algorithm obtained the best results compared with both its composing algorithms and a set of reference algorithms that were proposed for the special issue