Welcome to Swarm Intelligence community

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Swarm Intelligence is a field of Computer science. It is a form of Artificial intelligence. Some animals, mostly insects like ants, or bees form large colonies. These colonies are made of many animals that communicate with each other. Each animal is relatively simple, but by working together with other animals it is able to solve complex tasks.

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Brain storm optimization (BSO), first intended for simulating the human brainstorming process, is a relatively new algorithm in the SI community. As with the other metaphor-based swarm algorithms, the BSO algorithm makes few or no assumptions about the problem being optimized and solves a problem by having a population of solutions instead of a single one. Also, the BSO algorithm does not require that the optimization problem be differentiable as is required by classic optimization methods, which has made the BSO algorithm to be a highly effective way to the problems in the real-world community.

The canonical BSO employs three strategies: clustering operator, creating operator and selecting operator. Specifically, clustering operator organizes solutions into groups whose members are similar among them and are dissimilar to the solutions belonging to other clusters in some way. Creating operator generates new candidate solutions by combining existing ones according to its simple formulae. Selecting operator directly determines which solutions will be kept into the next generation according to the scores or fitness on the optimization problem. In this way, the optimization problem is treated as a black box that merely provides a measure of quality given a candidate solution and the gradient is therefore not needed.

**Description**

**Brain storm optimization (BSO)**