Preference-inspired co-evolutionary algorithms using weight vectors

Rui Wang, Robin C. Purshouse and Peter J. Fleming

European Journal of Operational Research, 2015, vol. 243, issue 2, 423-441

Abstract: Decomposition based algorithms perform well when a suitable set of weights are provided; however determining a good set of weights a priori for real-world problems is usually not straightforward due to a lack of knowledge about the geometry of the problem. This study proposes a novel algorithm called preference-inspired co-evolutionary algorithm using weights (PICEA-w) in which weights are co-evolved with candidate solutions during the search process. The co-evolution enables suitable weights to be constructed adaptively during the optimisation process, thus guiding candidate solutions towards the Pareto optimal front effectively. The benefits of co-evolution are demonstrated by comparing PICEA-w against other leading decomposition based algorithms that use random, evenly distributed and adaptive weights on a set of problems encompassing the range of problem geometries likely to be seen in practice, including simultaneous optimisation of up to seven conflicting objectives. Experimental results show that PICEA-w outperforms the comparison algorithms for most of the problems and is less sensitive to the problem geometry.

Keywords: Evolutionary algorithms; Multi-objective optimisation; Many-objective; Co-evolution; Weights (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (11)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:243:y:2015:i:2:p:423-441

DOI: 10.1016/j.ejor.2014.05.019

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