Large scale optimization based on co-ordinated bacterial dynamics and opposite numbers

Authors:
Jaydeep Ghosh Chowdhury;Aritra Chowdhury;Arghya Sur
Affiliations:
Department of Electronics and Telecommunication Engg., Jadavpur University, Kolkata, India;Department of Electronics and Telecommunication Engg., Jadavpur University, Kolkata, India;Department of Electronics and Telecommunication Engg., Jadavpur University, Kolkata, India
Venue:
SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
Year:
2012

Citing 4
Cited 0

A Simple Modification in CMA-ES Achieving Linear Time and Space Complexity

Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Differential evolution algorithm with strategy adaptation for global numerical optimization

IEEE Transactions on Evolutionary Computation
Tackling high dimensional nonseparable optimization problems by cooperatively coevolving particle swarms

CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Self-adaptive differential evolution with multi-trajectory search for large-scale optimization

Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on scalability of evolutionary algorithms and other metaheuristics for large-scale continuous optimization problems

Quantified Score

Hi-index	0.00

Visualization

Abstract

This work, named Large Scale Optimization based on co-ordinated Bacterial Dynamics and Opposite Numbers (LSCBO) presents a very fast algorithm to solve large scale optimization problems. The computational simplicity of the algorithm allows it to achieve admirable results. There are only three searching agents in the population, one being the primary bacterium and the other two are secondary bacteria. The proposed algorithm is employed on 7 benchmark functions of CEC2008 and it gives better results compared to the other well known contemporary algorithms present in the literature. The main reason for this is that the computational burden of the algorithm is significantly reduced.