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Agent Based Evolutionary Algorithms Applied to Constrained Multi-Objective Optimization and Decision-Making

Author: DingHui
Tutor: LiHongGuang
School: Beijing University of Chemical Technology
Course: Control Science and Engineering
Keywords: Agent Evolutionary algorithm Multi-objective optimization Multi-objective decision - making Constraints
CLC: TP18
Type: Master's thesis
Year: 2011
Downloads: 42
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Abstract


Goal of multi-objective optimization problem often conflicting , inconsistency of the optimization problem solution is not unique. Due to the presence of the constraint condition , so that the feasible solution space becomes discontinuous , resulting in the search of the optimal solution greatly increase the difficulty . Evolutionary algorithm is a parallel search optimization method for multi- objective optimization problem . But constraints remain a great challenge to the evolutionary algorithm search speed and solution quality . Agent because of its autonomy , coordination and self-organizing ability , can be used to improve the performance of the evolutionary algorithm solving . This in-depth research , the main contents include : 1, introduced the idea of the Agent to evolutionary algorithms , define the attributes of the Agent , so that it has the intelligence , distributed in the space of feasible not only be able to learn from the related evolutionary information can also be obtained from the environment , and promote the search for Pareto optimal solution ; 2 , the design constraints for the operation : the degree of constraint violation as an optimization objective , which the Agent has the energy impact ; optimal solution set set and optimal feasible solution set two external storage set order to keep the Agent population diversity in the optimization process , making the optimal solution will not be lost during evolution ; feasible solution , as well as the degree of constraint violation Agent local climbing operation , to facilitate the search of the optimal feasible solution ; 3 , multi- objective decision making and optimization evolutionary algorithm based on Agent combination of improvements on the properties of the Agent and optimize operations , respectively, a fusion of the posterior priority the evolutionary priority technical feasibility , and issues related to the simulation study , to obtain a satisfactory conclusion . 4 , the proposed multi-objective optimization algorithm applied to the welding beam reducer design optimization problem , the experimental results show that , at the same time to ensure the quality of the solution and the distribution , the algorithm can converge fast to the optimal solution , verify the effectiveness of the algorithm .

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