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Spider Monkey Optimization
(SMO) is a recent addition in the field of nature inspired optimization algorithms developed by Bansal et al. [1] SMO is based on the intelligent foraging behaviour of spider monkeys. SMO can be broadly classified as a computational intelligence technique for global optimization.
Background
editBefore, designing a new swarm intelligence based algorithm, it must understand that whether a behaviour is swarm intelligence or not. Two approaches Division of Labour and Self-Organization are the necessary and sufficient conditions for obtaining intelligent swarming behaviours mentioned by Karaboga et.al.
Development of SMO
editThis page is under progress.
Algorithm
editMain steps of Spider Monkey Optimization algorithm(SMO) Similar to the other population-based algorithms, SMO is a trial and error based collaborative iterative process.
There are two important parameter of this algorithm:
1) GlobalLeaderLimit.
2) LocalLeaderLimit.