Scalability of optimization algorithms is a major challenge in coping with the ever-growing
size of optimization problems in a wide range of application areas from high-dimensional …

Metaheuristic algorithms are extensively recognized as effective approaches for solving high-
dimensional optimization problems. These algorithms provide effective tools with important …

Practical optimization problems often involve a large number of variables, and solving them
in a reasonable amount of time becomes a challenge. Competitive swarm optimizer (CSO) is …

Complex continuous optimization problems widely exist nowadays due to the fast
development of the economy and society. Moreover, the technologies like Internet of things …

Over the ages, nature has constantly been a rich source of inspiration for science, with much
still to discover about and learn from. Swarm Intelligence (SI), a major branch of artificial …

Large-scale optimization problems (LSOPs) have drawn researchers' increasing attention
since their resemblance to real-world problems. However, due to the complex search space …

The first powerful variant of the Harris hawks optimization (HHO) is proposed in this work.
HHO is a recently developed swarm-based stochastic algorithm that has previously shown …

A comprehensive learning particle swarm optimizer (CLPSO) embedded with local search
(LS) is proposed to pursue higher optimization performance by taking the advantages of …

There exist many multiobjective optimization problems (MOPs) containing a large number of
decision variables in real-world applications, which are known as large-scale MOPs. Due to …

Social learning plays an important role in behavior learning among social animals. In
contrast to individual (asocial) learning, social learning has the advantage of allowing …