High-dimensional multi-target oriented multi-population mixing evolution method

A hybrid evolution and population technology, applied in the field of optimization, can solve the problems of deviation of optimization results, easy to fall into local optimum, insufficient convergence, etc. Effect

A hybrid evolution and population technology, applied in the field of optimization, can solve the problems of deviation of optimization results, easy to fall into local optimum, insufficient convergence, etc. Effect

CN103942601AInactive Publication Date: 2014-07-23HARBIN ENG UNIV
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  • High-dimensional multi-target oriented multi-population mixing evolution method
  • High-dimensional multi-target oriented multi-population mixing evolution method
  • High-dimensional multi-target oriented multi-population mixing evolution method

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Embodiment Construction

[0013] The present invention is described in more detail below in conjunction with accompanying drawing example:

[0014] combine figure 1 , the high-dimensional multi-objective oriented multi-population mixed evolution method is characterized by transforming the high-dimensional complex multi-objective optimization problem into a simple single-objective optimization problem in multiple fixed directions, and using an improved orientation angle difference operator to enhance each fixed direction At the same time, the SBX operator is used to strengthen the information interaction between various directions, enhance the local search ability, and then greatly improve the global search ability of the whole method. In the final improved elite retention strategy, the convergence and distribution of the solution set can be balanced as much as possible. The fuzzy dominance transforms the comparison of multiple targets among individuals into the comparison of two values ​​of membership ...

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Abstract

The invention provides a high-dimensional multi-target oriented multi-population mixing evolution method. A fixed direction matrix covering a whole searching space is generated by means of a sine function, and high-dimensional multi-target optimization is turned into single-target optimization in each fixed direction; according to the concepts of leading bees and following bees in optimizing of an artificial bee colony, a multi-population mechanism is set, a following population is set for each direction, the optimal solutions of all directions are selected to constitute a leading population, and the leading population guides evolution searching of the following populations in all directions; a mixed evolution strategy is put forward, the convergence capacity in the fixed directions is enhanced by means of direction angle difference operators which are put forward, and meanwhile, local searching capacity is enhanced by means of SBX operators; an elitism strategy based on novel fuzzy domination is put forward to maintain the scale of an external archive set. According to the method, convergence and distributivity of the optimal solutions of high-dimensional multi-target optimization can be effectively improved, and the solving effect is not influenced by the number of targets.

Description

technical field [0001] The invention relates to an optimization method, in particular to an optimization method for high-dimensional multi-objectives. Background technique [0002] In multi-objective optimization, the improvement of one sub-objective may lead to the performance degradation of one or several other sub-objectives. In order to achieve the optimization of the overall objective, it is usually necessary to comprehensively consider the conflicting sub-objectives, that is, for each sub-objective compromise. Therefore, unlike single-objective optimization problems, multi-objective optimization does not have an absolute or unique best solution, but a set of optimal solutions composed of many Pareto optimal solutions. When the number of targets increases to 4 or more (called high-dimensional multi-target), the performance of these Pareto sorting methods will be greatly reduced, because as the number of targets increases, the individuals in the population do not domina...

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Application Information

Patent Timeline
23 Jul 2014
Publication
CN103942601A
IPC
G06N3/12
Inventors
毕晓君; 张永建