Deterministic approaches to simultaneously solve different interrelated optimisation problems lead to a\ngeneral class of nonlinear complementarity problem (NCP).Dueto differentiability and convexity requirements\nof the problems, sophisticated algorithms are introduced in literature. This paper develops an\nevolutionary algorithm to solve the NCPs. The proposed approach is a parallel search in which multiple\npopulations representing different agents evolve simultaneously whilst in contact with each other. In this\ncontext, each agent autonomously solves its optimisation programme while sharing its decisions with\nthe neighbouring agents and, hence, it affects their actions. The framework is applied to an environmental\nand an aerospace application where the obtained results are compared with those found in literature.\nThe convergence and scalability of the approach is tested and its search algorithm performance is analysed.\nResults encourage the application of such an evolutionary based algorithm for complementarity\nproblems and future work should investigate its development as well as its performance improvements.
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