An introduction to genetic algorithms
An introduction to genetic algorithms
Cloning: a novel method for interactive parallel simulation
Proceedings of the 29th conference on Winter simulation
Future Generation Computer Systems
The Ant Colony Metaphor for Searching Continuous Design Spaces
Selected Papers from AISB Workshop on Evolutionary Computing
Advanced Clone-Analysis to Support Object-Oriented System Refactoring
WCRE '00 Proceedings of the Seventh Working Conference on Reverse Engineering (WCRE'00)
Ant Colony Optimization
A hybrid search algorithm with heuristics for resource allocation problem
Information Sciences—Informatics and Computer Science: An International Journal
Genetic algorithm with ant colony optimization (GA-ACO) for multiple sequence alignment
Applied Soft Computing
A Novel Clonal Selection Algorithm and Its Application to Traveling Salesman Problem
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An efficient self-organizing map designed by genetic algorithms for the traveling salesman problem
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A review of clonal selection algorithm and its applications
Artificial Intelligence Review
An artificial immune system based algorithm to solve unequal area facility layout problem
Expert Systems with Applications: An International Journal
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Both the clonal selection algorithm (CSA) and the ant colony optimization (ACO) are inspired by natural phenomena and are effective tools for solving complex problems. CSA can exploit and explore the solution space parallely and effectively. However, it can not use enough environment feedback information and thus has to do a large redundancy repeat during search. On the other hand, ACO is based on the concept of indirect cooperative foraging process via secreting pheromones. Its positive feedback ability is nice but its convergence speed is slow because of the little initial pheromones. In this paper, we propose a pheromone-linker to combine these two algorithms. The proposed hybrid clonal selection and ant colony optimization (CSA-ACO) reasonably utilizes the superiorities of both algorithms and also overcomes their inherent disadvantages. Simulation results based on the traveling salesman problems have demonstrated the merit of the proposed algorithm over some traditional techniques.