This paper presents a hybrid PSO algorithm (Par-ticle Swarm Optimization) with an ILS (Iterated Local Search) operator for handling equality constraints problems in mono-objective optimization problems. The ILS can be used to locally search around the best solutions in some generations, exploring the attraction basins in small portions of the feasible set. This process can compensate the difficulty of the evolutionary algorithm to generate good solutions in zero-volume regions. The greatest advantage of the operator is the simple implementation. Experiments performed on benchmark problems shows improvement in accuracy, reducing the gap for the tested problems.
|Title of host publication||2018 IEEE Congress on Evolutionary Computation (CEC)|
|Publication status||Published - 4 Oct 2018|
|Event||2018 IEEE Congress on Evolutionary Computation (CEC) - Rio de Janeiro, Brazil|
Duration: 8 Jul 2018 → 13 Jul 2018
|Conference||2018 IEEE Congress on Evolutionary Computation (CEC)|
|City||Rio de Janeiro|
|Period||8/07/18 → 13/07/18|
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Mota, F. O., Almeida, V., Wanner, E., & Moreira, G. (2018). Hybrid PSO Algorithm with Iterated Local Search Operator for Equality Constraints Problems. In 2018 IEEE Congress on Evolutionary Computation (CEC)  IEEE. https://doi.org/10.1109/CEC.2018.8477884