An Optimization Method for Constrained Problems Based on Evolutionary Optimization Algorithms

Abstract

The invention relates to an optimization method for constrained problems based on an evolutionary optimization algorithm. For constrained optimization problems, this method adopts the method of penalty function to deal with constrained problems, and uses swarm intelligence optimization algorithm to optimize the function with penalty items. Using this optimization method, the penalty coefficient of each constraint item in the algorithm iteration process is jointly determined by the number of all individuals in the population that meet the constraint conditions, the degree of violation of constraints, and the sum of the objective functions of all individuals in the population. This avoids the problem that the penalty coefficient is too large or too small, prompts the algorithm to achieve a balance between the satisfaction of the constraints and the non-satisfaction of the individuals in the population as much as possible in each iteration, and avoids that the optimization of practical engineering problems cannot give a suitable penalty coefficient The problem.

Description

technical field [0001] The invention belongs to the field of operations research and relates to an optimization method for constrained problems based on an evolutionary optimization algorithm. Background technique [0002] Nonlinear constraint optimization problem is a kind of problem widely used in science and engineering fields, and it is relatively difficult to solve. The traditional methods for solving this kind of problems are usually gradient-based local search methods, which are only suitable for the case where the objective function and constraints are differentiable, and generally can only guarantee to find a local optimal solution. On the other hand, some nonlinear constrained optimization problems in the fields of science and engineering often require a large amount of calculation, which takes a long time and resources. [0003] In the 1990s, a large number of optimization methods based on evolutionary algorithms were gradually paid attention to. But current evo...

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Problems solved by technology

If the penalty parameter is too large, it may be possible to guarantee access to the feasible region, but it will reduce the exploration of the infeasible region, thus losing some valuable information provided by the infeasible solution; if the penalty parameter is too small, then many search times are used in the infeasible domain, because the penalty term will be ignored relative to the objective function

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