Abstract
Article history: Received July 2 2013 Received in revised format September 7 2013 Accepted September 15 2013 Available online September 23 2013 The present work proposes a multi-objective improved teaching-learning based optimization (MO-ITLBO) algorithm for unconstrained and constrained multi-objective function optimization. The MO-ITLBO algorithm is the improved version of basic teaching-learning based optimization (TLBO) algorithm adapted for multi-objective problems. The basic TLBO algorithm is improved to enhance its exploration and exploitation capacities by introducing the concept of number of teachers, adaptive teaching factor, tutorial training and self-motivated learning. The MO-ITLBO algorithm uses a grid-based approach to adaptively assess the non-dominated solutions (i.e. Pareto front) maintained in an external archive. The performance of the MO-ITLBO algorithm is assessed by implementing it on unconstrained and constrained test problems proposed for the Congress on Evolutionary Computation 2009 (CEC 2009) competition. The performance assessment is done by using the inverted generational distance (IGD) measure. The IGD measures obtained by using the MO-ITLBO algorithm are compared with the IGD measures of the other state-of-the-art algorithms available in the literature. Finally, Lexicographic ordering is used to assess the overall performance of competitive algorithms. Results have shown that the proposed MO-ITLBO algorithm has obtained the 1st rank in the optimization of unconstrained test functions and the 3rd rank in the optimization of constrained test functions. © 2013 Growing Science Ltd. All rights reserved
Highlights
Finding the global optimum value(s) of a problem involving more than one objective with conflicting nature arises in many scientific applications
Posteriori techniques provide a set of solutions with the search process of multi-objective optimization (MOO) problem for the decision making (Coello et al, 2007)
Two new search mechanisms are introduced in the teaching-learning based optimization (TLBO) algorithm in the form of tutorial training and selfmotivated learning
Summary
Finding the global optimum value(s) of a problem involving more than one objective with conflicting nature arises in many scientific applications. Posteriori techniques provide a set of solutions with the search process of MOO problem for the decision making (Coello et al, 2007) These techniques are divided into many sub-groups like independent sampling, aggregation selection, criterion selection, Pareto sampling, Pareto-based selection, Pareto rank and niche-based selection, Pareto elitist-based selection, and hybrid selection. Interactive Particle Swarm Optimization (IPSO) (Agrawal et al, 2008) Dynamic Multiple Swarms in Multi-Objective Particle Swarm Optimization (DSMOPSO) A multi-objective improved teaching-learning based optimization (MO-ITLBO) algorithm is proposed for multi-objective unconstrained and constrained optimization problems. Working of the TLBO algorithm is explained below with the teacher phase and learner phase
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