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

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

Multi-objective optimization problems are difficult to solve in that the optimized objectives are
usually conflicting with each other. It is usually hard to find an optimal solution that satisfies …

The aim of this study was to gather, discuss, and compare recently developed
metaheuristics to understand the pace of development in the field of metaheuristics and …

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 …

Intelligent vehicle testing has been a hotspot in the field of intelligent vehicles. Due to the
multi-dimensional parameters and the continuity of testing scenarios, a complete test of all …

Conventional and classical optimization methods are not efficient enough to deal with
complicated, NP-hard, high-dimensional, non-linear, and hybrid problems. In recent years …

Metaheuristic algorithms have been an interesting area for researchers, scientists and
academicians due to their specific and significant characteristics and capabilities. Their …

Metaheuristic algorithms have been an interesting and widely used area for scientists,
researchers and academicians because of their specific and significant characteristics and …

Inspired by the evolution process of human intelligence and the social learning theory, a
new swarm intelligence algorithm paradigm named the social learning optimization (SLO) …