A guidable bat algorithm based on doppler effect to improve solving efficiency for optimization problems

Abstract

A new guidable bat algorithm (GBA) based on Doppler Effect is proposed to improve problem-solving efficiency of optimization problems. Three searching polices and three exploration strategies are designed in the proposed GBA. The bats governed by GBA are enabled the ability of guidance by frequency shift based on Doppler Effect so that the bats are able to rapidly fly toward the current best bat in guidable search. Both refined search and divers search is employed to explore the better position near the current best bat and develop new searching area. These searching polices benefit discover the eligible position to upgrade the quality of position with the current best bat in a short time. In addition, next-generation evolutionary computing (EC 2.0) is created to breaks the bottleneck of traditional ECs to create the new paradigm in ECs. In EC 2.0, conflict theory is introduced to help the efficiency of solution discovery. Conflict between individuals is healthful behavior for population evolution. Constructive conflict promotes the overall quality of population. Conflict, competition and cooperation are the three pillars of collective effects investigated in this study. The context-awareness property is another feature of EC 2.0. The context-awareness indicates that the individuals are able to perceive the environmental information by physic laws.