Simulated annealing: theory and applications
Simulated annealing: theory and applications
Error-correcting codes and finite fields (student ed.)
Error-correcting codes and finite fields (student ed.)
Ant algorithms for discrete optimization
Artificial Life
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Swarm intelligence: power in numbers
Communications of the ACM - Evolving data mining into solutions for insights
Ant Colony Optimization
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Studying the locator polynomials of minimum weight codewords of BCH codes
IEEE Transactions on Information Theory
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In practical terms all coded electronic signals are prone to corruption during transmission but may be corrected by using error-correcting codes. The minimum distance of a code is important because it is the major parameter affecting the error-correcting performance of a code. In this paper a recent heuristic combinatorial optimisation algorithm, called ant colony optimisation (ACO), is applied to the problem of determining minimum distances of error-correcting codes. The ACO algorithm is motivated by analogy with natural phenomena, in particular, the ability of a colony of ants to 'optimise' their collective endeavours. In this paper the biological background for ACO is explained and its computational implementation is presented in an error-correcting code context. The particular implementation of ACO makes use of a tabu search (TS) improvement phase to give a computationally enhanced algorithm (ACOTS). Two classes of codes are then used to show that ACOTS is a useful and viable optimisation technique to investigate minimum distances of error-correcting codes.