Study on an Adaptive Co-Evolutionary ACO Algorithm for Complex Optimization Problems

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.

Punching trajectory optimization method for warp-knitted vamp based on improved ant colony optimization algorithm and Radau pseudospectral method

“…This positive feedback mechanism results in ants eventually choosing the shortest path to realize path planning. 25,26…”

“…This positive feedback mechanism results in ants eventually choosing the shortest path to realize path planning. 25,26 Assuming that the number of ants in the colony is m, and d ij (i, j = 1, 2,. . ., n) represents the distance between punching point i and j.…”

Optimization of multiple autonomous underwater vehicle cooperative system communication network topology based on total energy consumption

“…The ACO algorithm in intelligent algorithm is adopted in this study. The ACO algorithm 24 is proposed from the study of the ant colony behavior. It is an evolutionary simulation algorithm for solving optimization problems, such as the traveling salesman problem, and can strongly find improved solutions and achieve parallelism.…”

Efficient Chaotic Imperialist Competitive Algorithm with Dropout Strategy for Global Optimization

“…Several studies combine optimization algorithms to stochastic strategies, such as crossover or mutation [4][5][6][7]. On the contrary, some other studies [8][9][10][11] showed a tendency to apply the chaos system to stochastic optimization algorithms because information entropy is closely related to chaoticity. The chaotic imperialist competitive algorithm (CICA), which is an ICA-based algorithm combined with chaos theory, has shown excellent performance in global optimization problems [12].…”