Adaptive Group Collaborative Artificial Bee Colony Algorithm
This is an incremental improvement to artificial bee colony algorithms for optimization problems, primarily relevant to researchers in computational optimization.
The paper tackles the exploration-exploitation trade-off in artificial bee colony algorithms by introducing an adaptive group collaborative approach that dynamically assigns different search strategies to population groups, achieving superior accuracy and stability on benchmark functions and optimal solutions for complex scheduling problems.
As an effective algorithm for solving complex optimization problems, artificial bee colony (ABC) algorithm has shown to be competitive, but the same as other population-based algorithms, it is poor at balancing the abilities of global searching in the whole solution space (named as exploration) and quick searching in local solution space which is defined as exploitation. For improving the performance of ABC, an adaptive group collaborative ABC (AgABC) algorithm is introduced where the population in different phases is divided to specific groups and different search strategies with different abilities are assigned to the members in groups, and the member or strategy which obtains the best solution will be employed for further searching. Experimental results on benchmark functions show that the proposed algorithm with dynamic mechanism is superior to other algorithms in searching accuracy and stability. Furthermore, numerical experiments show that the proposed method can generate the optimal solution for the complex scheduling problem.