Reducing bias and inefficiency in the selection algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Hybrid fuzzy-genetic technique for multisensor fusion
Information Sciences: an International Journal
A Cooperative Coevolutionary Approach to Function Optimization
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Comparison between Genetic Algorithms and Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
A hybrid search algorithm with heuristics for resource allocation problem
Information Sciences—Informatics and Computer Science: An International Journal
Constraint handling in genetic algorithms using a gradient-based repair method
Computers and Operations Research
A hybrid genetic algorithm and bacterial foraging approach for global optimization
Information Sciences: an International Journal
Predictive models for the breeder genetic algorithm i. continuous parameter optimization
Evolutionary Computation
A Novel PSO-DE-Based Hybrid Algorithm for Global Optimization
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
Classification Techniques of Neural Networks Using Improved Genetic Algorithms
WGEC '08 Proceedings of the 2008 Second International Conference on Genetic and Evolutionary Computing
Handling constraints in particle swarm optimization using a small population size
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Crossed particle swarm optimization algorithm
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Extending particle swarm optimisation via genetic programming
EuroGP'05 Proceedings of the 8th European conference on Genetic Programming
Intelligent control of AVR system using GA-BF
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part IV
Gradual distributed real-coded genetic algorithms
IEEE Transactions on Evolutionary Computation
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
On the computation of all global minimizers through particle swarm optimization
IEEE Transactions on Evolutionary Computation
A hybrid of genetic algorithm and particle swarm optimization for recurrent network design
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Particle swarm optimisation-based support vector machine for intelligent fault diagnosis
International Journal of Computer Applications in Technology
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The social foraging behaviour of Escherichia coli bacteria and the effectiveness of genetic operators have recently been combined to develop a hybridised algorithm for distributed optimisation and control. The classical algorithms have their importance in solving real-world optimisation problems. Hybridisation of two algorithms is gaining popularity among researchers to explore the area of optimisation. This paper proposes a novel algorithm which hybridises the best features of three basic algorithms, i.e., genetic algorithm (GA), bacterial foraging (BF) and particle swarm optimisation (PSO) as genetically bacterial swarm optimisation (GBSO). The hybridisation is carried out in two phases; first, the diversity in searching the optimal solution is increased using selection, crossover and mutation operators. Secondly, the search direction vector is optimised using PSO to enhance the convergence rate of the fitness function in achieving the optimality. The proposed algorithm is tested on a set of functions which are then compared with the basic algorithms. Simulation results were reported and the proposed algorithm indeed has established superiority over the basic algorithms with respect to the set of functions considered and it can easily be extended for other global optimisation problems.