Performance assessment of the hybrid Archive-based Micro Genetic Algorithm (AMGA) on the CEC09 test problems

Abstract

Abstract-In this paper, the performance assessment of the hybrid Archive-based Micro Genetic Algorithm (AMGA) on a set of bound-constrained synthetic test problems is reported. The hybrid AMGA proposed in this paper is a combination of a classical gradient based single-objective optimization algorithm and an evolutionary multi-objective optimization algorithm. The gradient based optimizer is used for a fast local search and is a variant of the sequential quadratic programming method. The Matlab implementation of the SQP (provided by the fmincon optimization function) is used in this paper. The evolutionary multi-objective optimization algorithm AMGA is used as the global optimizer. A scalarization scheme based on the weighted objectives is proposed which is designed to facilitate the simultaneous improvement of all the objectives. The scalarization scheme proposed in this paper also utilizes reference points as constraints to enable the algorithm to solve non-convex optimization problems. The gradient based optimizer is used as the mutation operator of the evolutionary algorithm and a suitable scheme to switch between the genetic mutation and the gradient based mutation is proposed. The hybrid AMGA is designed to balance local versus global search strategies so as to obtain a set of diverse non-dominated solutions as quickly as possible. The simulation results of the hybrid AMGA are reported on the bound-constrained test problems described in the CEC09 benchmark suite.

A multi-objective differential evolution approach based on ε-elimination uniform-diversity for mechanism design

“…The proposed algorithm executed 30 times for each test function and the average results obtained by MUDE are compared with the results of all the algorithms participating in the CEC 2009 competition (Zhang et al 2009b;Chen et al 2009;Huang et al 2009;Liu and Li 2009;Gao et al 2009;Kukkonen and Lampinen 2009;Qu and Suganthan 2009;Sindhya et al 2009;Tiwari et al 2009;Tseng and Chen 2009;Wang et al 2009;Zamuda et al 2009), as well as two other more recent multi-objective optimization algorithms (Venkata Rao and Patel 2013; Akbari and Ziarati 2012). The performance indicator used to quantify the quality of the obtained results is the IGD (Inverted Generational distance) Metric (Zhang et al 2009a).…”

An Improved Ranking Scheme for Selection of Parents in Multi-Objective Genetic Algorithm

“…The hybrid AMGA (Archive-based Micro Genetic Algorithm) [21] is a constrained multi-objective evolutionary optimization algorithm. For the purpose of selection, AMGA uses a two-tier fitness assignment mechanism; the primary fitness is the rank, which is based on the domination level, and the secondary fitness is based on the diversity of the solutions in the entire population.…”

An improved version of the multiple trajectory search for real value multi-objective optimization problems

“…The following algorithms were presented in this competition: MOEADGM (Chen, Chen, and Zhang 2009), OMOEAII (Gao et al 2009), OWMOSaDE (Huang et al 2009), GDE3 (Kukkonen and Lampinen 2009), LiuLi Algorithm , DMOEADD ), MOEA/D (Zhang, Liu, and Li 2009), MOEP (Qu and Suganthan 2009), NSGAII-LS (Sindhya et al 2009), AMGA (Tiwari et al 2009), multiple trajectory search (MTS) (Tseng and Chen 2009), ClusteringMOEA (Wang et al 2009) and DECMOSA-SQP (Zamuda et al 2009). In the competition on unconstrained multi-objective optimization, MOEA/D was the winner and MTS was ranked the second best.…”