Abstract
The ant colony optimization (ACO) algorithm has the characteristics of positive feedback, essential parallelism, and global convergence, but it has the shortcomings of premature convergence and slow convergence speed. The co-evolutionary algorithm (CEA) emphasizes the existing interaction among different sub-populations, but it is overly formal, and does not form a very strict and unified definition. Therefore, a new adaptive co-evolutionary ant colony optimization (SCEACO) algorithm based on the complementary advantages and hybrid mechanism is proposed in this paper. Firstly, the pheromone update formula is improved and the pheromone range of the ACO algorithm is limited in order to achieve the adaptive update of the pheromone. The elitist strategy and co-evolutionary idea are used for reference, the symbiotic mechanism and hybrid mechanism are introduced to better utilize the advantages of the CEA and ACO. Then the multi-objective optimization problem is divided into several sub-problems, each sub-problem corresponds to one population. Each ant colony is divided into multiple sub-populations in a common search space, and each sub-population performs the search activity and pheromone updating strategy. The elitist strategy is used to retain the elitist individuals within the population and the min-max ant strategy is used to set pheromone concentration for each path. Next, the selection, crossover, and mutation operations of individuals are introduced to adaptively adjust the parameters and implement the information sharing of the population and the co-evolution. Finally, the gate assignment problem of a hub airport is selected to verify the optimization performance of the SCEACO algorithm. The experiment results show that the SCEACO algorithm can effectively solve the gate assignment problem of a hub airport and obtain the effective assignment result. The SCEACO algorithm improves the convergence speed, and enhances the local search ability and global search capability.
Highlights
A large number of problems in the fields of industry, agriculture, national defense, information, transportation, economy, management, and so on, can be transformed into optimization problems [1].Symmetry 2018, 10, 104; doi:10.3390/sym10040104 www.mdpi.com/journal/symmetryoptimization problems are some of the more popular and difficult problems in the world.At present, there are no mature theories and methods to effectively solve these optimization problems.As an important branch of scientific research, the optimization methods have made a great impact on the development of many disciplines [2]
The idea of co-evolution, the elitist strategy, the min-max ant strategy, the symbiosis mechanism, and the hybrid mechanism are introduced into the co-evolutionary algorithm (CEA) and ACO algorithms in order to form the complementation of advantages and propose an adaptive co-evolutionary ant colony optimization (SCEACO) algorithm in this paper
The ACO algorithm takes on the positive feedback, essential parallelism, and global convergence in solving optimization problems, but it has undetermined parameters, premature stagnation, and slow convergence speed
Summary
A large number of problems in the fields of industry, agriculture, national defense, information, transportation, economy, management, and so on, can be transformed into optimization problems [1]. As the most effective methods in solving optimization problems, the ant colony optimization (ACO) algorithm and co-evolution algorithm have received extensive attention and research. The CEA can reasonably divide the optimization problem space, and can effectively jump out of the local optimum value and find the best optimal solution for a large-scale optimization problem It emphasizes the existing interaction among different sub-populations, affecting each other and coevolving together [11,12]. In order to realize the complementary advantages, the elitist strategy, the min-max ant strategy, co-evolutionary idea, the symbiotic mechanism, and the hybrid mechanism are introduced into the CEA and ACO algorithms in order to propose a new adaptive co-evolutionary ant colony optimization (SCEACO) algorithm.
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