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Various metaheuristic optimization algorithms are being developed to obtain optimal solutions to real-world problems. Metaheuristic algorithms are inspired by various metaphors, resulting in different search mechanisms, operators, and parameters, and thus algorithm-specific strengths and weaknesses. Newly developed algorithms are generally tested using benchmark problems. However, for existing traditional benchmark problems, it is difficult for users to freely modify the characteristics of a problem. Thus, their shapes and sizes are limited, which is a disadvantage. In this study, a modified Gaussian fitness landscape generator is proposed based on a probability density function, to make up for the disadvantages of traditional benchmark problems. The fitness landscape developed in this study contains a total of six features and can be employed to easily create various problems depending on user needs, which is an important advantage. It is applied to quantitatively evaluate the performance and reliability of eight reported metaheuristic algorithms. In addition, a sensitivity analysis is performed on the population size for population-based algorithms. Furthermore, improved versions of the metaheuristic algorithm are considered, to investigate which performance aspects are enhanced by applying the same fitness landscape. The modified Gaussian fitness landscape generator can be employed to compare the performances of existing optimization algorithms and to evaluate the performances of newly developed algorithms. In addition, it can be employed to develop methods of improving algorithms by evaluating their strengths and weaknesses. [ABSTRACT FROM AUTHOR]

In this paper, the water cycle algorithm (WCA), a recently developed metaheuristic method is proposed for solving multi-objective optimization problems (MOPs). The fundamental concept of the WCA is inspired by the observation of water cycle process, and movement of rivers and streams to the sea in the real world. Several benchmark functions have been used to evaluate the performance of the WCA optimizer for the MOPs. The obtained optimization results based on the considered test functions and comparisons with other well-known methods illustrate and clarify the robustness and efficiency of the WCA and its exploratory capability for solving the MOPs. [ABSTRACT FROM AUTHOR]