Abstract
A.new evolutionary algorithm is proposed for solving multi-objective optimization problems, focusing on the issue of developing a diverse population of non-dominated solutions. The key new approach in this algorithm is to use a diversity-emphasizing probabilistic approach in determining whether an off-spring individual is considered in the replacement selection phase, along with the use of a non-domination ranking scheme. This evolutionary multi-objective crowding algorithm (EMOCA) is evaluated using nine bench-mark multi-objective optimization problems and shown to produce non-dominated solutions with significant diversity, outperforming three state-of-the-art multi-objective evolutionary algorithms on most ofthe test problems.
Original language | English (US) |
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Pages (from-to) | 107-123 |
Number of pages | 17 |
Journal | Journal of Intelligent Systems |
Volume | 17 |
Issue number | 1-3 |
DOIs | |
State | Published - 2008 |
Keywords
- Benchmark test functions
- Diversity
- Evolutionary algorithms
- Multi-objective optimization
- Non-domination
- Pareto-optimization
ASJC Scopus subject areas
- Software
- Information Systems
- Artificial Intelligence