Spider Monkey Optimization

Spider Monkey Optimization (SMO) is a recent addition in the field of nature inspired optimization algorithms developed by Bansal et al. SMO is based on the intelligent foraging behavior of spider monkeys. SMO can be broadly classified as a computational intelligence technique for global optimization.
Introduction
The name swarm is used for an accumulation of creatures such as ants, fish, birds, termites and honey bees which behave collectively. The definition given by Bonabeau for the swarm intelligence is “any attempt to design algorithms or distributed problem-solving devices inspired by the collective behavior of social insect colonies and other animal societies” . Swarm intelligence is a meta-heuristic approach in the field of nature inspired techniques that is used to solve optimization problems. It is based on the collective behavior of social creatures. Social creatures utilize their ability of social learning and adaptation to solve complex tasks. Researchers have analyzed such behaviors and designed algorithms that can be used to solve nonlinear, non-convex or combinatorial optimization problems in many science and engineering domains. Previous research have shown that algorithms based on Swarm Intelligence have great potential to find a near optimal solution of real world optimization problem. The algorithms that have been emerged in recent years are Ant Colony Optimization (ACO) , Particle Swarm Optimization (PSO) , Bacterial Foraging
Optimization (BFO) , Artificial Bee Colony Optimization (ABC) etc.In order to design a new swarm intelligence based algorithm, it is necessary to understand whether a behavior is swarm intelligent behavior or not. Karaboga et al. mentioned that Division of Labor and Self-Organization are the necessary and sufficient conditions for obtaining intelligent swarming behaviors.
Self-organization: is an important feature of a swarm structure which results in global level response by means of interactions among its low-level components without a central authority or external element enforcing it through planning. Therefore, the globally coherent pattern appears from the local interaction of the components that build up the structure, thus the organization is achieved in parallel
as all the elements act at the same time and distributed as no element is a central coordinator. Bonabeau et al.have defined the following four important characteristics on which self-organization is based :
Positive feedback:is an information extracted from the output of a system and reapplied to the input to promotes the creations of convenient structures. In the field of swarm intelligence positive feedback provides diversity and accelerate the system to new stable state.
Negative feedback: compensates the effect of positive feedback and helps to stabilize the collective pattern.
Fluctuations: are the rate or magnitude of random changes in the system. Randomness is often crucial for efflorescent structures since it allows the findings
of new solutions. In foraging process, it helps to get-ride of stagnation.
Multiple interactions: provide the way of learning from the individuals within a society and thus enhance the combined intelligence of the swarm.
2. Division of labor: is a cooperative labor in specific, circumscribed tasks and like roles. In a group, there are various tasks, which are performed simultaneously by specialized individuals. Simultaneous task performance by
cooperating specialized individuals is believed to be more
efficient than the sequential task performance by unspecialized individuals .
This paper proposes a new swarm intelligence algorithm
based on the foraging behavior of spider monkeys. The for-
aging behavior of spider monkeys shows that these monkeys fall in the category of fission-fusion social structure
(FFSS) based animals. Thus the proposed optimization algorithm which is based on foraging behavior of spider monkeys
is explained better in terms of FFSS. Further, the proposed
strategy is tested on various benchmark and engineering optimization test problems.
The rest of the paper is organized as follows: Sect. 2
describes the foraging behavior and social structure of spider
monkeys. In Sect. 3, first, the foraging behavior is critically
evaluated to be a swarm intelligent behavior over the necessary and sufficient conditions of swarm intelligence and then
Spider Monkey Optimization algorithm is proposed. A detail
discussion about the proposed strategy is presented in Sect. 4.
In Sect. 5, performance of the proposed strategy is analyzed
and compared with four state-of-the-art algorithms, namely
DE, PSO, ABC and CMA-ES. Finally, in Sect. 6, paper is
concluded.

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