Adaptive step-size based Spider Monkey Optimization
Garima Hazrati, HARISH KUMAR SHARMA, Nirmala Sharma · 2016
Spider Monkey Optimization (SMO) algorithm is recent swarm intelligence based meta-heuristic technique to solve the continuous optimization problems. Many times, it suffers from the problem of slow convergence. To improve the exploitation abilities and evading premature convergence, a modified variant of SMO is proposed. The modified variant is known as adaptive step-size based spider monkey optimization (AsSMO) algorithm. In position update process of AsSMO, step-size is calculated by the fitness of a spider monkey. The prominent fit solutions will converge quickly in comparison to the non-prominent fit solutions. The proposed algorithm is compared with SMO and self-adaptive spider monkey optimization (SaSMO) over 15 benchmark functions and reported results show that AsSMO is a spell variant among them.