High-dimensional multi-target set evolutionary optimization method based on preference of decision maker

Abstract

The invention relates to a high-dimensional multi-target set evolutionary optimization method based on preference of a decision maker. According to the method, the objective function of an original optimization problem is converted into an expectation function according to the preferential area of each target given by the decision maker; the expectation function optimization problem is converted into a two-target optimization problem with a set formed by multiple solutions of the original optimization problem as a new decision variable and the hypervolume and the satisfaction degree of the preference of the decision maker as a new objective function; an internal self-adaptive crossing strategy of individuals of the set is designed according to the hypervolume contribution degree of the solutions of the original optimization problem in the set and the satisfaction degree of the preference of the decision maker; furthermore, an individual variation strategy of the set is designed by means of the updating of particles in the PSO algorithm and the idea of a globally optimal solution and a locally optimal solution, so that a Pareto optimal solution set satisfying the preference of the decision maker and meeting the requirement for convergence and distributivity balance is obtained.

Description

technical field [0001] This patent belongs to the field of evolutionary optimization, and specifically relates to a high-dimensional multi-objective set evolutionary optimization method based on decision maker preferences, which can be used to solve high-dimensional multi-objective optimization problems in practical optimization problems. Background technique [0002] In the real world, there are multi-objective optimization problems that need to optimize multiple objectives at the same time. In most cases, these multiple objectives that are optimized at the same time conflict with each other. When the number of objective functions is more than three, it is called a high-dimensional multi-objective optimization problem. Such problems are very common, such as underground water pipe design, backpacks, and circuit component layout. The traditional evolutionary optimization algorithm based on Pareto dominance relationship is no longer applicable simply because as the number of ...

AI Technical Summary

Problems solved by technology

However, since these methods do not consider the preference of the decision maker, what is still looking for is the entire Pareto front

As mentioned earlier, finding this front is usually difficult and unnecessary

In addition, how to design a suitable ensemble evolutionary strategy is very important, and there are still few related research results

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