Local Search, Multiobjective Optimization and the Pareto Archived Evolution Strategy
Joshua D. Knowles, David Corne · 1999
The Pareto Archived Evolution Strategy (PAES) [KC99c, KC99b], a local search algorithm for multiobjective optimization tasks, is compared with a modern, proven population-based EA, the Strength Pareto Evolutionary Algorithm (SPEA) of Zitzler and Thiele [ZT98, ZDT99, ZT99]. Comparison is carried out with respect to six test functions designed by Deb, each of which is designed to capture and isolate a specific problem feature that may present difficulties to multiobjective optimizers. Statistical techniques introduced previously, and derived from those of Fonseca and Fleming [FF96], are used to process the results to form confidence measures relating to the percentage of the non-dominated front which is covered by each algorithm. These results indicate that, with no attempt at tuning PAES to any of the problems, it outperforms SPEA conclusively on four of the test functions. Some investigation of the mutation rates used by PAES is then carried out. This shows that using a higher mutation...