SSO: A Hybrid Swarm Intelligence Optimization Algorithm

AbstractSwarm intelligence is a captivating area in the field of optimization for the researchers. Many researchers have proposed several algorithms inspired from nature by analysing the swarming behaviour of various creatures like ants, animals, birds and more. A hybrid approach for optimization is proposed by modelling the swarm behaviour of spider monkeys and squirrels where both of these are nature-inspired algorithms for looking out food. The spider monkey optimization is coherent for training and is adaptable in swarm intelligence algorithms and enhanced computational speed. On the other hand, the squirrel search algorithm is inspired by squirrels which have a repetitive process for lifetime of searching for food, and this behaviour is formulated mathematically and using gliding technique for locomotion. With these two algorithms, a new algorithm is proposed named spider squirrel optimization, and it is compared with similar algorithms on different test problems and evaluated using convergence rate analysis and statistical analysis. The results demonstrate that the proposed algorithm shows promising convergence rate and mean absolute error as compared to other existing optimizers.KeywordsSwarm intelligenceOptimizationGlidingSpider monkey optimizationSquirrel search algorithm and Spider Squirrel Optimization