Title: A review of multi-objective optimisation and decision making using evolutionary algorithms

Authors: Muneendra Ojha; Krishna Pratap Singh; Pavan Chakraborty; Shekhar Verma

Addresses: Department of Computer Science and Engineering, Dr. S.P.M. International Institute of Information Technology, Naya Raipur, Raipur, Chhattisgarh, 493661, India ' Department of Information Technology, Indian Institute of Information Technology, Allahabad, Allahabad, Uttar Pradesh, 211012, India ' Department of Information Technology, Indian Institute of Information Technology, Allahabad, Allahabad, Uttar Pradesh, 211012, India ' Department of Information Technology, Indian Institute of Information Technology, Allahabad, Allahabad, Uttar Pradesh, 211012, India

Abstract: Research in the field of multi-objective optimisation problem (MOP) has garnered ample interest in the last two decades. Majority of methods developed for solving the problem belong to the class of evolutionary algorithms (EA) which are population-based evolution search strategies involving exploration and exploitation in general. Multi-criteria decision making (MCDM) is another aspect of MOP which involves finding methods to help a decision maker (DM) in making most optimal decisions in a conflicting scenario. In this paper, we present a brief review of the methods and techniques developed in the last 15 years which try to solve the MOP and MCDM problems. The strengths and weaknesses of methods have been discussed to present a holistic view. This paper covers challenges associated with MOEAs, different solution approaches such as Pareto-based methods and non-Pareto methods, indicator-based methods, aggregation methods, decomposition-based methods, methods using reference sets, MOEAs involving DM, a priori, interactive and a posteriori preference incorporation methods. It also discusses most of the quality metrics and performance indicators proposed in the literature along with benchmark problems. In addition, some future research issues and directions are also presented.

Keywords: multi-objective optimisation review; genetic algorithm; evolutionary algorithms; multi-criteria decision making; MCDM.

DOI: 10.1504/IJBIC.2019.101640

International Journal of Bio-Inspired Computation, 2019 Vol.14 No.2, pp.69 - 84

Accepted: 13 Aug 2018
Published online: 19 Aug 2019 *

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