Abstract
To achieve effective and accurate optimization for multi-objective optimization problems, a multi-objective artificial bee colony algorithm with regulation operators (RMOABC) inspired by the intelligent foraging behavior of honey bees was proposed in this paper. The proposed algorithm utilizes the Pareto dominance theory and takes advantage of adaptive grid and regulation operator mechanisms. The adaptive grid technique is used to adaptively assess the Pareto front maintained in an external archive and the regulation operator is used to balance the weights of the local search and the global search in the evolution of the algorithm. The performance of RMOABC was evaluated in comparison with other nature inspired algorithms includes NSGA-II and MOEA/D. The experiments results demonstrated that the RMOABC approach has better accuracy and minimal execution time.
Highlights
Most of the optimization problems in the real world are multi-objective optimization problems.Multi-objective optimization is an area of multiple criteria decision making which often have multiple objectives to simultaneously optimize that are conflicting
Researchers have carried out many interesting studies in the multi-objective optimization field to handle complex nonlinear multi-objective problems, such as the multi-objective evolutionary algorithms (MOEAs), multi-objective PSO (MPSO), and so on
External archive is a common mechanism of multi-objective algorithms based on Pareto theory to keep a historical record of the non-dominated solutions found along the search process [34]
Summary
Most of the optimization problems in the real world are multi-objective optimization problems. Multi-objective optimization is an area of multiple criteria decision making which often have multiple objectives to simultaneously optimize that are conflicting. It has been applied in many fields of science, including engineering, economics and logistics where optimal decisions need to be made in the presence of trade-offs between two or more conflicting objectives [1]. If a multi-objective problem is solved by a single-objective optimization method, it can only reflect one aspect of the problem. In the field of hydrological model parameters’ optimization, the single-objective optimization algorithm cannot fully reflect the hydrological process and feature [2]. Researchers have carried out many interesting studies in the multi-objective optimization field to handle complex nonlinear multi-objective problems, such as the multi-objective evolutionary algorithms (MOEAs), multi-objective PSO (MPSO), and so on
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