Bi-preference linkage-driven artificial bee colony algorithm with multi-operator fusion

Abstract

The artificial bee colony algorithm (ABC) struggles in handling complex optimization problems with high dimensions in light of its search operators’ strong exploration and weak exploitation properties. To tackle this situation, in this study, we propose a bi-preference linkage-driven ABC algorithm with multi-operator fusion, named BPLABC. BPLABC couples a preference-free stochastic search operator with a global best-guided search operator in the employed bee phase to maintain the population diversity while enhancing the population quality. During the onlooker bee phase, a tailored bi-type elite-guided exploitation mechanism is employed to regulate the exploitation intensity of the promising elite nectar sources selected via a new roulette selection probability calculation paradigm. To discourage the onlooker bees from slipping into local traps, after the scout bee phase, an auxiliary adversarial search operator is assembled to tug certain promising elite solutions away from the present pseudo-global best solution. To illustrate the effectiveness and efficiency of BPLABC, two sets of test suits consisting of 23 benchmark problems, 30 complex CEC2014 functions, and two real-world problems are picked for testing. Experimental results showed that BPLABC can achieve superior or equivalent performance to several representative ABC variants on the majority of the tested problems.