Pareto optimization using the struggle genetic crowding algorithm
2002 (English)In: Engineering optimization (Print), ISSN 0305-215X, Vol. 34, no 6, 623-643 p.Article in journal (Refereed) Published
Many real-world engineering design problems involve the simultaneous optimization of several conflicting objectives. In this paper, a method combining the straggle genetic crowding algorithm with Pareto-based population ranking is proposed to elicit trade-off frontiers. The new method has been tested on a variety of published problems, reliably locating both discontinuous Pareto frontiers as well as multiple Pareto frontiers in multi-modal search spaces. Other published multi-objective genetic algorithms are less robust in locating both global and local Pareto frontiers in a single optimization. For example, in a multi-modal test problem a previously published non-dominated sorting GA (NSGA) located the global Pareto frontier in 41% of the optimizations, while the proposed method located both global and local frontiers in all test runs. Additionally, the algorithm requires little problem specific tuning of parameters.
Place, publisher, year, edition, pages
2002. Vol. 34, no 6, 623-643 p.
Genetic algorithms, Multi-objective optimization, Pareto optimization
IdentifiersURN: urn:nbn:se:liu:diva-46816DOI: 10.1080/03052150215721OAI: oai:DiVA.org:liu-46816DiVA: diva2:267712