Disruption operator-based spider monkey optimization algorithm
Avinash Kaur, HARISH KUMAR SHARMA, Nirmala Sharma · 2017
Spider monkey optimization (SMO) algorithm, is a neoteric Swarm Intelligence based algorithm, inducemented from the social comportment of spider monkeys, commonly known as fission-fusion social system. Despite of perpetuating an equilibrium state betwixt intensification and diversification by its own, SMO sometimes converges at a particular point due to its swarming nature. To overcome this problem, a new stage, namely disruption stage is incorporated with SMO. The proffered variant is named as Disruption operator-based spider monkey optimization (DiSMO) algorithm. In the incorporated stage, the disruption operator helps to scatter the swarms in convergence condition and according to the difference from the best solution, this operator is employed to entire solutions so that the solutions might attract or distract from the best solution. Further, the efficiency of the proffered strategy is estimated over 12 different benchmark functions and the outputs are being compared with basic SMO, its significant variant: power law based local search in SMO (PLSMO), and particle swarm optimization (PSO).