Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
A hybrid genetic algorithm and bacterial foraging approach for global optimization
Information Sciences: an International Journal
Comparison among five evolutionary-based optimization algorithms
Advanced Engineering Informatics
Transmission loss reduction based on FACTS and bacteria foraging algorithm
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
A hybrid least square-fuzzy bacterial foraging strategy for harmonic estimation
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 Effective PSO-Based Memetic Algorithm for Flow Shop Scheduling
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Optimization methods have been developed by many researchers. Especially, the methods based on Evolutionary Algorithms (EAs) have received increased attention from diversity fields. Recently, bacterial foraging algorithm mimicked bacterial behavior has been introduced by Passino. However, his work did not implement an important bacterial behavior regulating division so-called 'quorum-sensing'. The quorum-sensing is a chemical communication including producing, releasing, detecting, and responding to small hormone-like molecules termed autoinducers. This communication allows bacteria to coordinate the behavior of the group. This paper proposes an optimization algorithm based on the bacterial quorum sensing. In order to estimate the performance of the algorithm, we use a multimodal Gaussian having several minima and maxima, and also four benchmark functions. Experimental results show that this proposed method can achieve better performance than conventional bacterial foraging algorithm.