Abstract
This article introduces a modern optimization algorithm to solve optimization problems. Group Optimization (GO) is based on concept that uses all agents to update population of algorithm. Every agent of population could to be used for population updating. For these purpose two groups is specified for any agent. One group for good agents and another group for bad agents. These groups is used for updating position of each agent. twenty-three standard benchmark test functions are evaluated using GO and then results are compared with eight other optimization method.
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