Rain-fall optimization algorithm: A population based algorithm for solving constrained optimization problems

This paper proposes rain-fall optimization algorithm (RFO), a new nature-inspired algorithm based on behavior of raindrops, for solving of real-valued numerical optimization problems. RFO has been developed from a motivation to find a simpler and more effective search algorithm to optimize multidimensional numerical test functions. It is effective in searching and finding an optimum solution from a large search domain within an acceptable CPU time. Statistical analysis compared the solution quality with well-known heuristic search methods. In addition, an economic dispatch (ED) optimization problem is run on an IEEE 30-bus test system, and the results, compared with those of recent optimization methods, show RFO performing relatively well, sufficiently effective to solve engineering problems. The constraint-handling strategy of the proposed method for solving ED problem is to generate and work with feasible solutions along all the optimization iterations without any mismatch between electricity demand and the total amount of power generation. Unlike the penalty methods, this strategy is unaffected by parameter setting of applied optimization method and its applicability for solving constrained optimization problems is not hampered. Eventually, its robustness is validated by the results of a sensitivity analysis of the parameters. (C) 2016 Elsevier B.V. All rights reserved.