Modified spider monkey optimization

Spider Monkey Optimization is a well known meta-heuristic in the arena of nature inspired algorithms. It is basically known for its stagnation removal power in its original design. To balance the meta-heuristics mechanisms while preserving premature convergence, a new variant is developed which is named as Modified spider monkey optimization. In this paper, metropolis principle is used from simulated annealing which improves the global search capability of algorithm. In addition to this strength of spider monkey is used for maintaining the step-size to enhance the convergence speed. The intended algorithm is tested over 10 benchmarks functions and compared with Spider monkey optimization, particle swarm optimization and one of its recent variant Self-adaptive spider monkey optimization.