A Review of Major Application Areas of Differential Evolution

Plagianakos, Vassilis P.; Tasoulis, Dimitris K.; Vrahatis, Michael N.

doi:10.1007/978-3-540-68830-3_8

Cited by 80 publications

(29 citation statements)

References 43 publications

(45 reference statements)

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“…simple operator to generate new member, easy implementation and fast convergence cause widespread applications of it in the fields of optimization problems (Liu et al 2007;Chang and Low 2007;Chiou 2007;Strinivas and Rangaiah 2007;Babu and Munawar 2007;Plagianakos et al 2008). Despite various applications of the DE, this algorithm has a problem to find high accurate optimal solution, indeed it finds efficiently the neighborhood of global optimal point, but for many cases, it is not able to converge exactly to individual optimal point.…”

Section: Introductionmentioning

confidence: 97%

Three modified versions of differential evolution algorithm for continuous optimization

2010

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Differential evolution (DE) is one simple and effective evolutionary algorithm (EA) for global optimization. In this paper, three modified versions of the DE to improve its performance, to repair its defect in accurate converging to individual optimal point and to compensate the limited amount of search moves of original DE are proposed. In the first modified version called bidirectional differential evolution (BDE), to generate a new trial point, is used from the bidirectional optimization concept, and in the second modified version called shuffled differential evolution (SDE), population such as shuffled frog leaping (SFL) algorithm is divided in to several memeplexes and each memeplex is improved by the DE algorithm. Finally, in the third modified version of DE called shuffled bidirectional differential evolution (SBDE) to improve each memeplex is used from the proposed BDE algorithm. Three proposed modified versions are applied on two types of DE and six obtained algorithms are compared with original DE and SFL algorithms. Experiments on continuous benchmark functions and non-parametric analysis of obtained results demonstrate that applying bidirectional concept only improves one type of the DE. But the SDE and the SBDE have a better success rate and higher solution precision than original DE and SFL, whereas those are more time consuming on some functions. In a later part of the comparative experiments, a comparison of the proposed algorithms with some modern DE and the other EAs reported in the literature confirms a better or at least comparable performance of our proposed algorithms.

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Section: Introductionmentioning

confidence: 97%

Three modified versions of differential evolution algorithm for continuous optimization

2010

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“…However, proper setting of control parameters in the DE is not an easy task. In addition to these attractive characteristics, simplicity and easy implementation are two main preferences of DE than other EAs and a basic reason for widespread application of DE on different optimization problems in recent years (see Plagianakos et al 2008). Feoktistov (2006), from an algorithmic viewpoint, mentioned the reasons for the success of DE: the success of DE is due to an implicit self-adaptation contained within the algorithmic structure.…”

Section: The De: Benefits and Drawbacksmentioning

confidence: 98%

Opposition-based learning in the shuffled differential evolution algorithm

Ahandani

Alavi-Rad

2012

Soft Comput

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This paper proposes using the opposition-based learning (OBL) strategy in the shuffled differential evolution (SDE). In the SDE, population is divided into several memeplexes and each memeplex is improved by the differential evolution (DE) algorithm. The OBL by comparing the fitness of an individual to its opposite and retaining the fitter one in the population accelerates search process. The objective of this paper is to introduce new versions of the DE which, on one hand, use the partitioning and shuffling concepts of SDE to compensate for the limited amount of search moves of the original DE and, on the other hand, employ the OBL to accelerate the DE without making premature convergence. Four versions of DE algorithm are proposed based on the OBL and SDE strategies. All algorithms similarly use the opposition-based population initialization to achieve fitter initial individuals and their difference is in applying opposition-based generation jumping. Experiments on 25 benchmark functions designed for the special session on real-parameter optimization of CEC2005 and non-parametric analysis of obtained results demonstrate that the performances of the proposed algorithms are better than the SDE. The fourth version of proposed algorithm has a significant difference compared to the SDE in terms of all considered aspects. The emphasis of comparison results is to obtain some successful performances on unsolved functions for the first time, which so far have not been reported any successful runs on them. In a later part of the comparative experiments, performance comparisons of the proposed algorithm with some modern DE algorithms reported in the literature confirm a significantly better performance of our proposed algorithm, especially on high-dimensional functions.

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“…An application to highway network capacity optimization is given in Koh (2009). A review of DE applications is presented in Plagianakos (2008).…”

Section: Differential Evolution: a Surveymentioning

confidence: 99%

Recent advances in differential evolution: a survey and experimental analysis

2009

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Differential Evolution (DE) is a simple and efficient optimizer, especially for continuous optimization. For these reasons DE has often been employed for solving various engineering problems. On the other hand, the DE structure has some limitations in the search logic, since it contains too narrow a set of exploration moves. This fact has inspired many computer scientists to improve upon DE by proposing modifications to the original algorithm. This paper presents a survey on DE and its recent advances. A classification, into two macro-groups, of the DE modifications is proposed here: (1) algorithms which integrate additional components within the DE structure, (2) algorithms which employ a modified DE structure. For each macro-group, four algorithms representative of the state-of-the-art in DE, have been selected for an in depth description of their working principles. In order to compare their performance, these eight algorithm have been tested on a set of benchmark problems. Experiments have been repeated for a (relatively) low dimensional case and a (relatively) high dimensional case. The working principles, differences and similarities of these recently proposed DE-based algorithms have also been highlighted throughout the paper. Although within both macro-groups, it is unclear whether there is a superiority of one algorithm with respect to the others, some conclusions can be drawn. At first, in order to improve upon the DE performance a modification which includes some additional and alternative search moves integrating those contained in a standard DE is necessary. These extra moves should assist the DE framework in detecting new promising search directions to be used by DE. Thus, a limited employment of these alternative moves appears to be the best option in successfully assisting DE. The successful extra moves are obtained in two ways: an increase in the exploitative pressure and the introduction of some randomization. This randomization should not be excessive though, since it would jeopardize the search. A proper increase in the randomization is crucial for obtaining significant improvements in the DE functioning.123 62 F. Neri, V. Tirronen Numerical results show that, among the algorithms considered in this study, the most efficient additional components in a DE framework appear to be the population size reduction and the scale factor local search. Regarding the modified DE structures, the global and local neighborhood search and self-adaptive control parameter scheme, recently proposed in literature, seem to be the most promising modifications.

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A Review of Major Application Areas of Differential Evolution

Cited by 80 publications

References 43 publications

Three modified versions of differential evolution algorithm for continuous optimization

Three modified versions of differential evolution algorithm for continuous optimization

Opposition-based learning in the shuffled differential evolution algorithm

Recent advances in differential evolution: a survey and experimental analysis

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