Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
A non-dominated sorting particle swarm optimizer for multiobjective optimization
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Teaching the applications of optimisation in game theory's zero sum and non-zero sum games
International Journal of Data Analysis Techniques and Strategies
Optimization based on bacterial chemotaxis
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Handling multiple objectives with particle swarm optimization
IEEE Transactions on Evolutionary Computation
RM-MEDA: A Regularity Model-Based Multiobjective Estimation of Distribution Algorithm
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A multidimensional scaling localisation algorithm based on bacterial foraging optimisation
International Journal of Wireless and Mobile Computing
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This paper focuses on the multi-objective optimisation problem MOOP. To improve the convergence speed and diversity of bacterial chemotaxis multi-objective optimisation algorithm BCMOA and overcome the defects of escape from local minimum, this paper proposes an improved bacterial colony chemotaxis multi-objective optimisation algorithm IBCCMOA. Firstly, fast non-dominated sorting approach is used to initialise the position of all the bacterias. Secondly, colony intelligent optimisation thought is adopted. Thirdly, a strategy of elite reserve is applied to avoid abandoning the points that the original position is good. Experimental results show that the convergence and diversity solutions of the proposed algorithm are better than that of the existing BCMOA.