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Research And Application Of Multi-objective Artificial Bee Colony Algorithm

Posted on:2020-12-21Degree:DoctorType:Dissertation
Country:ChinaCandidate:S J MaFull Text:PDF
GTID:1368330620952338Subject:Intelligent Environment Analysis and Planning
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Many problems in scientific research and engineering applications involve Multiple objectives to be optimized simultaneously,which are collectively known as Multi-objective optimization problems.Since such problems are often multiple objectives that need to be optimized simultaneously,solving multi-objective optimization problems is extremely difficult.Therefore,how to quickly obtain an efficient solution has become an important topic in the multi-objective optimization field.Although the traditional optimization algorithm has the advantages of high computational efficiency,strong reliability,and maturity,it has insurmountable limitations.Although the traditional optimization algorithms have the advantages of high computational efficiency,strong reliability,and maturity,it has insurmountable limitations.In recent years,the emergence of swarm intelligence algorithms has brought new opportunities to address multi-objective optimization problems.Due to its versatility,robustness,and global search capabilities,many multi-objective swarm intelligence algorithms have been proposed.Artificial bee colony algorithm is a high-performance swarm intelligence algorithm.It has been applied to many engineering problems and has achieved good optimization results.However,the relevant theories and methods of multiobjective artificial bee colony are still in the initial stage.The main results can be summarized as follows:(1)In this part,the multiobjective permutation flow shop scheduling problem with sequence dependent setup times has been an object of investigations for decades.This widely studied problem from the scheduling theory links the sophisticated solution algorithms with the moderate real world applications.This paper presents a novel multiobjective discrete artificial bee colony algorithm based decomposition,called MODABC/D,to solve the sequence dependent setup times multiobjective permutation flowshop scheduling problem with the objective to minimize makespan and total flowtime.First,in order to make the standard artificial bee colony algorithm to solve the scheduling problem,a discrete artificial bee colony algorithm is proposed to solve the problem based on the perturbation operation.Then,a problem-specific solution builder heuristic is used to initialize the population to enhance the quality of the initial solution.Finally,a further local search method are comprised of a single local search procedures based on the insertion neighborhood structures to find the better solution for the nonimproved individual.The performance of the proposed algorithms is tested on the well-known benchmark suite of Taillard.The highly effective performance of the multiobjective discrete artificial bee colony algorithm-based decomposition is compared against the state of art algorithms from the existing literature in terms of both coverage value and hypervolume indicator.(2)In this part,environmental/Economic dispatch is a complicated non-linear constrained problem which plays an important role in power system operation.Different kinds of approaches have been proposed to solve this problem and obtained some achievements in previous study.In this paper,a new multi-objective global best artificial bee colony algorithm is proposed,which has balance the exploration capability and the stochastic exploitation ability to create new trail vector.A crowing distance-based approach is introduced to update the individual and to update the external archive.Moreover,a new technique is introduced to handle the unfeasible solutions directly.Numerical results indicate that the performance of the MOGABC presents the best results when compared with previous optimization methods in solving Environmental/Economic dispatch problems.(3)In this part,artificial bee colony(ABC)is a simple and effective global optimization algorithm.It has been successfully applied to solve a wide range of real-world optimization problem and later it was extended to constrained design problems as well.This paper described a modified artificial bee colony algorithm for constrained optimization problem based on multiobjective optimization method.The employed bee colony is partitioned into several subpopulation and severed as the search engine for each subpopulation.Then,the onlooker bee colony can explore the new search space.For constraint handling,Method based on multiobjective optimization is used to handle the objective function.Pareto dominance used in multiobjective optimization is used to compare the individuals in the population.In order to verify the performance of our approach,12 well-known constrained problems and three chemical engineering problems are employed.Experimental results indicate that the proposed algorithm performs better than,or at least comparable to state-of-the-art approaches in terms of the quality of the resulting solutions from literature.
Keywords/Search Tags:Genetic algorithm, particle swarm optimization, artificial bee colony algorithm, differential evolution algorithm, scheduling problem, environmental/economic Power Dispatch problem, constrained optimization problem
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