As the world moves towards industrialization, optimization problems become more
challenging to solve in a reasonable time. More than 500 new metaheuristic algorithms …

Abstract The Mountain Gazelle Optimizer (MGO), a novel meta-heuristic algorithm inspired
by the social life and hierarchy of wild mountain gazelles, is suggested in this paper. In this …

Metaheuristics play a critical role in solving optimization problems, and most of them have
been inspired by the collective intelligence of natural organisms in nature. This paper …

Research in metaheuristics for global optimization problems are currently experiencing an
overload of wide range of available metaheuristic-based solution approaches. Since the …

Activity-based scheduling optimization is a combinatorial problem built on the traveling
salesman problem intending to optimize people schedules considering their trips and the …

In recent years, the optimization community has witnessed a surge in the popularity of
population-based optimization methods. However, many of these methods suffer from …

Metaheuristic algorithms are computational intelligence paradigms especially used for
sophisticated solving optimization problems. This chapter aims to review of all …

For high-dimensional data, the traditional feature selection method is slightly inadequate. At
present, most of the existing hybrid search methods have problems of high computational …

In recent algorithmic family simulates different biological processes observed in Nature in
order to efficiently address complex optimization problems. In the last years the number of …

Abstract The Tunicate Swarm Algorithm (TSA) inspires by simulating the lives of Tunicates at
sea and how food is obtained. This algorithm is easily entrapped to local optimization …