A Nature Inspired Optimization Algorithm


Swarm intelligence is a fascinating area for the researchers in the field of optimization. Researchers have developed many algorithms by simulating the swarming behavior of various creatures like ants, honey bees, fishes, birds and their findings are very motivating. In this paper, a new approach for optimization is proposed by modeling the social behavior of spider monkeys. Spider monkeys have been categorized as fission-fusion social structure based animals. The animals which follow fission-fusion social systems, initially work in a large group and based on need after some time, they divide themselves in smaller groups led by an adult female for foraging. Therefore, the proposed strategy broadly classified as inspiration from the intelligent foraging behavior of fission-fusion social structure based animals.


Social behavior of spider monkeys inspires authors to develop an stochastic optimization technique that mimics the foraging behavior of spider monkeys. The foraging 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 can be explained better in terms of FFSS. Following are the key features of the FFSS.
  1. The fission-fusion social structure based animals are social and live in groups of 40-50 individuals. The FFSS of swarm may reduce the foraging competition among group members by dividing them into sub-groups in order to search food.
  2. A female (global Leader) generally leads the group and is responsible for searching food sources. If she is not able to get enough food for the group, she divides the group into smaller subgroups (size varies from 3 to 8 members) that forage independently.
  3. Sub-groups are also supposed to be leaded by a female (local leader) who becomes decision-maker for planning an efficient foraging route each day.
  4. The group members communicate among themselves and with other group members, to maintain social bonds and territorial boundaries.
In the developed strategy, foraging behavior of FFSS based animals (e.g. spider monkeys) is divided into four steps. First, the group starts food foraging and evaluates their distance from the food. In the second step, based on the distance from the foods, group members update their positions and again evaluate distance from the food sources. Furthermore, in the third step, local leader updates its best position within the group and if the position is not updated for a specified number of times then all members of that group start searching of the foods in different directions. Next, in the fourth step, global leader, updates its ever best position and in case of stagnation, it splits the group into smaller size subgroups. All the four steps mentioned aforesaid, are continuously executed until the desired output is achieved. There are two important control parameters necessary to introduce in the proposed strategy, one is GlobalLeaderLimit and another is LocalLeaderLimit which helps local and global leaders to take appropriate decisions.

SMO Social Behavior

SMO Algorithm/ Pseudo Code

SMO Flowchart

Jagdish C Bansal

Harish Sharma

Shimpi Singh Jadon

Maurice Clerc

Sr No. Available Downloads     Actions
1.Spider Monkey Optimization Algorithm for Numerical Optimization
2.SMO matlab Code [RAR-compressed file]
3.SMO C++ Code [zip-compressed file]
4.SMO Python Code [zip-compressed file]