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ensemble of Constraint Handling Techniques

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2010年第4期14卷 561-579页

作者： Mallipeddi, Rammohan Suganthan, Ponnuthurai N. Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

During the last three decades, several constraint handling techniques have been developed to be used with evolutionary algorithms (EAs). According to the no free lunch theorem, it is impossible for a single constraint handling technique to outperform all other techniques on every problem. In other words, depending on several factors such as the ratio between feasible search space and the whole search space, multimodality of the problem, the chosen EA, and global exploration/local exploitation stages of the search process, different constraint handling methods can be effective during different stages of the search process. Motivated by these observations, we propose an ensemble of constraint handling techniques (ECHT) to solve constrained real-parameter optimization problems, where each constraint handling method has its own population. A distinguishing feature of the ECHT is the usage of every function call by each population associated with each constraint handling technique. Being a general concept, the ECHT can be realized with any existing EA. In this paper, we present two instantiations of the ECHT using four constraint handling methods with the evolutionary programming and differential evolution as the EAs. Experimental results show that the performance of ECHT is better than each single constraint handling method used to form the ensemble with the respective EA, and competitive to the state-of-the-art algorithms.

关键词： Constrained optimization differential evolution ensemble of constraint handling techniques ensemble of optimization algorithms evolutionary programming

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An ensemble of discrete differential evolution algorithms for solving the generalized traveling salesman problem

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APPLIED MATHEMATICS AND COMPUTATION 2010年第9期215卷 3356-3368页

作者： Tasgetiren, M. Fatih Suganthan, P. N. Pan, Quan-Ke Yasar Univ Dept Ind Engn Izmir Turkey Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore Liaocheng Univ Coll Comp Sci Liaocheng Shandong Peoples R China

In this paper, an ensemble of discrete differential evolution algorithms with parallel populations is presented. In a single populated discrete differential evolution (DDE) algorithm, the destruction and construction (DC) procedure is employed to generate the mutant population whereas the trial population is obtained through a crossover operator. The performance of the DDE algorithm is substantially affected by the parameters of DC procedure as well as the choice of crossover operator. In order to enable the DDE algorithm to make use of different parameter values and crossover operators simultaneously, we propose an ensemble of DDE (eDDE) algorithms where each parameter set and crossover operator is assigned to one of the parallel populations. Each parallel parent population does not only compete with offspring population generated by its own population but also the offspring populations generated by all other parallel populations which use different parameter settings and crossover operators. As an application area, the well-known generalized traveling salesman problem (GTSP) is chosen, where the set of nodes is divided into clusters so that the objective is to find a tour with minimum cost passing through exactly one node from each cluster. The experimental results show that none of the single populated variants was effective in solving all the GTSP instances whereas the eDDE performed substantially better than the single populated variants on a set of problem instances. Furthermore, through the experimental analysis of results, the performance of the eDDE algorithm is also compared against the best performing algorithms from the literature. Ultimately, all of the best known averaged solutions for larger instances are further improved by the eDDE algorithm. (C) 2009 Elsevier Inc. All rights reserved.

关键词： Generalized traveling salesman problem Metaheuristic Discrete differential evolution algorithm ensemble of optimization algorithms Evolutionary algorithms

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