ARTINT
2862
S0004-3702(15)00099-5
10.1016/j.artint.2015.06.007
The Authors
Bi-goal evolution for many-objective optimization problems
Miqing
Li
a
Shengxiang
Yang
b
⁎
syang@dmu.ac.uk
Xiaohui
Liu
a
a
Department of Computer Science, Brunel University, London UB8 3PH, UK
Department of Computer Science
Brunel University
London
UB8 3PH
UK
b
Centre for Computational Intelligence (CCI), School of Computer Science and Informatics, De Montfort University, Leicester LE1 9BH, UK
Centre for Computational Intelligence (CCI)
School of Computer Science and Informatics
De Montfort University
Leicester
LE1 9BH
UK
⁎
Corresponding author.
Abstract
This paper presents a meta-objective optimization approach, called Bi-Goal Evolution (BiGE), to deal with multi-objective optimization problems with many objectives. In multi-objective optimization, it is generally observed that 1) the conflict between the proximity and diversity requirements is aggravated with the increase of the number of objectives and 2) the Pareto dominance loses its effectiveness for a high-dimensional space but works well on a low-dimensional space. Inspired by these two observations, BiGE converts a given multi-objective optimization problem into a bi-goal (objective) optimization problem regarding proximity and diversity, and then handles it using the Pareto dominance relation in this bi-goal domain. Implemented with estimation methods of individuals' performance and the classic Pareto nondominated sorting procedure, BiGE divides individuals into different nondominated layers and attempts to put well-converged and well-distributed individuals into the first few layers. From a series of extensive experiments on four groups of well-defined continuous and combinatorial optimization problems with 5, 10 and 15 objectives, BiGE has been found to be very competitive against five state-of-the-art algorithms in balancing proximity and diversity. The proposed approach is the first step towards a new way of addressing many-objective problems as well as indicating several important issues for future development of this type of algorithms.
Keywords
Evolutionary multi-objective optimization
Many-objective optimization
Proximity
Diversity
Bi-goal evolution
KBJ00000000002806
2015-08-21T19:32:56
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10.1016/j.artint.2015.06.007
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