A hybrid evolutionary metaheuristics (HEMH) applied on 0/1 multiobjective knapsack problems

Abstract

Handling Multiobjective Optimization Problems (MOOP) using Hybrid Metaheuristics represents a promising and interest area of research. In this paper, a Hybrid Evolutionary Metaheuristics (HEMH) is presented. It combines different metaheuristics integrated with each other to enhance the search capabilities. It improves both of intensification and diversification toward the preferred solutions and concentrates the search efforts to investigate the promising regions in the search space. In the proposed HEMH, the search process is divided into two phases. In the first one, the DM-GRASP is applied to obtain an initial set of high quality solutions dispersed along the Pareto front. Then, the search efforts are intensified on the promising regions around these solutions through the second phase. The greedy randomized path-relinking with local search or reproduction operators are applied to improve the quality and to guide the search to explore the non discovered regions in the search space. The two phases are combined with a suitable evolutionary framework supporting the integration and cooperation. Moreover, the efficient solutions explored over the search are collected in an external archive. The HEMH is verified and tested against some of the state of the art MOEAs using a set of MOKSP instances commonly used in the literature. The experimental results indicate that the HEMH is highly competitive and can be considered as a viable alternative.

Hybrid Metaheuristics based on MOEA/D for 0/1 multiobjective knapsack problems: A comparative study

“…The competitive results achieved by discrete DE in [16] motivated us to hybrid discrete DE within MOEA/D framework. Moreover, path-relinking could improve the search if it applied on high quality solutions [10]. This work is partially related to our previous work in [10] in which a new hybrid approach (HEMH) was developed.…”

“…Moreover, path-relinking could improve the search if it applied on high quality solutions [10]. This work is partially related to our previous work in [10] in which a new hybrid approach (HEMH) was developed. But here, we only concentrate on studying the effect of incorporating differential evolution and/or path-relinking in the MOEA/D.…”

Oil in Syria between Terrorism and Dictatorship

“…A scalar fitness function was utilized to cull a couple of parent solutions to engender incipient solutions with crossover and mutation operator. Hybrid evolutionary metaheuristics (HEMH) [77] presents a combination with different metaheuristics, each one integrated to other to improve the search capabilities. The hybridization of greedy randomized adaptive search procedure (GRASP) with data mining (DM-GRASP) [78] is directed to a primary set with high quality solutions discrete along the Pareto front within the framework of MOEA/D.…”

ClusPath: a temporal-driven clustering to infer typical evolution paths

“…We formulate the problem of parameter tunning as a multi-objective optimization problem, optimizing in the 4-dimensional space of evaluation measures, with the parameters of ClusPath serving as internal variables over which the search is performed. Solving multiobjective optimization problems using evolutionary algorithms (MOEAs) has been investigated by many authors (Deb et al 2002;Halsall-Whitney and Thibault 2006;Kafafy et al 2011;Mihȃiţȃ et al 2014;Zitzler et al 2001). Pareto dominance based MOEAs such as NSGAII (Deb et al 2002), SPEA2 (Zitzler et al 2001) and HEMH (Kafafy et al 2011) have been dominantly used in the recent studies.…”

“…Solving multiobjective optimization problems using evolutionary algorithms (MOEAs) has been investigated by many authors (Deb et al 2002;Halsall-Whitney and Thibault 2006;Kafafy et al 2011;Mihȃiţȃ et al 2014;Zitzler et al 2001). Pareto dominance based MOEAs such as NSGAII (Deb et al 2002), SPEA2 (Zitzler et al 2001) and HEMH (Kafafy et al 2011) have been dominantly used in the recent studies. In multiobjective optimization, the set of Pareto optimal solutions is approximated using a large number of non-dominated points.…”