Many-objective directed evolutionary line search

Authors:
Evan James Hughes
Affiliations:
Cranfield University, Swindon, United Kingdom
Venue:
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Year:
2011

Citing 6
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Abstract

Algorithms capable of performing efficient and controllable many objective optimisation are becoming more necessary as the complexity of optimisation problems to be solved increases. This paper describes a new algorithm that combines elements of traditional gradient based optimisation methods along with a powerful many-objective capable search process. The algorithm exploits the directed line search (such as Golden Section Search) procedures found in many single-objective gradient based algorithms in order to both explore and exploit features in the optimisation landscape. The target vector and aggregation methods used in the MSOPS algorithm have been employed to provide effective and controllable many-objective optimisation, especially suited to close interaction with a designer where it is often desired to target specific regions of the Pareto front. The Many Objective Directed Evolutionary Line Search (MODELS) algorithm is demonstrated on a constrained function with a concave Pareto front in up to 20 dimensions and is shown to outperform existing optimisers, some of which are known to perform well for many-objective problems.