Abstract
This article introduces the Immigrant Population Search Algorithm (IPSA) inspired by the pattern of human population migration to find better habitats. The algorithm is viewed as a new optimization method for solving constrained optimization problems, and it belongs to the set of population-based algorithms that are proposed for combinatorial optimization. In this algorithm, the life environment is the solution space of the problem. Every point of this space is a solution for the problem, which may be feasible or infeasible, and the quality of life at that point is the value of fitness function for that solution. Each population group tries to investigate feasible and better habitats. In other words, it tries to optimize the problem. After the algorithm steps are described, the efficiency of the algorithm is compared to that of three other metaheuristic algorithms that are used to optimize some mathematic problems. The results show that the proposed algorithm performs better than the other three methods.
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