Multi-preference high dimension object optimization method based on co-evolution

Abstract

A multi-preference high dimension object optimization method based on co-evolution comprises the following steps: 1, initializing parameters; 2, updating; 3, using an ASF expansion function to map theideal solution in an evolution population into a target space, using same as a preference vector to guide the population to evolve in a reference direction, using the preference area selection strategy to obtain two temporary reference points and further builds a decision maker interested area ROI, determining the upper and lower boundary scopes in preference vector set generation, and using a co-evolution mechanism to guide the population to converge to the preference area; 4, using the co-evolution mechanism to optimize the target function, and selecting N candidate solutions with the maximum adaptation value from the candidate solutions in the ROI area scope and entering the next generation evolution; 5, determining whether termination conditions are met or not, if not, returning to step 2; if yes, outputting the optimal solution set, and ending the algorithm operation. The solution set obtained by the method has better convergence, has strong transplantability, and has better implementations.

Description

technical field [0001] The invention relates to a multi-preference high-dimensional object optimization method based on co-evolution, which attempts to solve the situation that the proportion of non-dominated solutions is too high in the high-dimensional object optimization problem from the perspective of decision maker preference. Background technique [0002] Multi-Objective Evolutionary Algorithm (MOEA) is one of the effective ways to solve multi-objective optimization problems. Representative algorithms include NSGA-II proposed by Deb, SPEA2 proposed by Zitzler and PAES proposed by Knowles. However, with the complexity of practical problems, the solution of the problem needs to meet the common constraints of many aspects, making the original relatively simple two-objective or three-objective problem evolve into a high-dimensional objective problem. As the target dimension increases, the proportion of non-dominated solutions in the population increases rapidly, resulting...

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Problems solved by technology

[0003] In order to overcome the problem of poor algorithm convergence when the existing multi-objective evolutionary algorithm solves high-dimensional object optimization problems, the present invention provides a multi-preference high-dimensional object optimization method based on co-evolution, through the co-evolution mode of population and preference vector set , guide the population to approach the optimal Pareto front that the decision maker is interested in, improve the convergence of the algorithm, and at the same time provide better options for the decision maker

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