多目標連續型仿水流優化演算法

Abstract

The newly developed optimization algorithm, Water Flow-like Algorithm, that is WFA, simulates a solution searching agent as a water flow traversing the lowest point of a terrain. The number of water flows is dynamically changed while water flows split into subflows against rough terrain and merge several flows into one single flow. Flow splitting and merging are mimicked by the WAF to conduct efficient optimum search in the solution space. In addition, water evaporation and precipitation are simulated in WFA to jump out of local optima or to broaden the searching area. This paper presents a WFA for Multi-objective Continuous Optimization Problems, namely WFA4MC. This paper presents three merging methods for different merging conditions. First, the location-based merging approach is frequently adopted in general optimization problems, either continuous or discrete ones. In addition to the location-based approach, we propose an objective-based merging approach for our multi-objective optimization problems, where a set of non-dominated solutions with objective values dispersedly distributed in the objective space is preferred. In order to prove WFA4MC performances precisely, this research proposes Correctness and Coverness to measure non-dominated solutions in ZDT functions. Besides, the Generational Distance is used in the comparison with other heuristic Algorithms. The result showed that based on the same limit of the number of objective function calls, the WFA4MC outperform than others.