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In this study, we present an effective hybrid algorithm based on particle swarm optimization (PSO) and teaching-learning-based optimization (TLBO), called PSO-TLBO, to solve multi-objective optimization (MOO) problems. In PSO-TLBO, teacher phase and learner phase in TLBO are incorporated to a PSO to improve the exploration and exploitation ability. Another feature of PSO-TLBO is the improved circular...
Decomposition-based methods are an increasingly popular choice for a posteriori multi-objective optimization. However the ability of such methods to describe a trade-off surface depends on the choice of weighting vectors defining the set of subproblems to be solved. Recent adaptive approaches have sought to progressively modify the weighting vectors to obtain a desirable distribution of solutions...
Convergence towards, and diversity across the Pareto-optimal front are the two main requirements when optimising a multiobjective optimisation problem (MOP) with conflicting objectives. Most established multiobjective evolutionary algorithms (MOEAs) have mechanisms that address these requirements. However, in many-objective optimisation, where the number of objectives is greater than 2 or 3, it has...
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