@techreport{Deb2003a,
abstract = {The trade-off between obtaining a well-converged and well-distributed set of pareto-optimal solutions and obtaining them in a small computational time is an important issue in multi-objective evolutionary optimization. Most multi-objective evolutionary algorithms (MOEAs) developed in the past decade are either good for achieving a well-distributed solutions at the expense of a large computational effort or computationally fast at the expense of achieving a not-so-good distribution of solutions. For example, although SPEA produces a much better distribution compared to NSGA-II, the computational time needed to run SPEA is much larger. In this paper, we propose a steady-state MOEA, based on the espilon-dominance concept and efficient parent and archive update strategies, for the purpose of developing a compromised algorithm for achieving a well distributed set of solutions quickly. Based on an extensive comparative study with three other state-of-the-art MOEAs and a clustered NSGA-II suggested in this paper on a number of two, three, and four objective test problems, it is observed that the proposed steady-state MOEA is a good compromise in terms of convergence near to the Pareto-optimal front, diversity of solutions, and computational time. Moreover, the proposed epsilon-MOEA is a step closer towards making MOEAs pragmatic, particularly allowing a decision-maker to control the achievable accuracy in the obtained Pareto-optimal solutions.},
author = {Deb, Kalyanmoy and Mohan, Manikanth and Mishra, Shikhar},
institution = {Kanpur Genetic Algorithms Laboratory, Indian Institute of Technology Kanpur, Kalyanpur, Kanpur, Uttar Pradesh 208016, India},
title = {{A fast multi-objective evolutionary algorithm for finding well-spread pareto-optimal solutions}},
url = {https://coin-lab.org/content/publications.html},
number = {2003002},
year = {2003}
}