This review aims to exploit a study on different benchmark test functions used to evaluate the
performance of Meta-Heuristic (MH) optimization techniques. The performance of the MH …

Learning and optimization are the two essential abilities of human beings for problem
solving. Similarly, computer scientists have made great efforts to design artificial neural …

To fully exploit the strong exploitation of differential evolution (DE) and the strong exploration
of the estimation-of-distribution algorithm (EDA), an improved differential evolution by …

To develop new meta-heuristic algorithms and evaluate on the benchmark functions is the
most challenging task. In this paper, performance of the various developed meta-heuristic …

Evolutionary multitasking (EMT) exploits the correlation among different tasks to help handle
them through knowledge transfer (KT) techniques in evolutionary algorithms. In this area …

In this paper the adaptive differential evolution algorithm is presented, which includes a set
of concepts, such as linear bias change in parameter adaptation, repetitive generation of …

This paper presents an enhanced version of NL-SHADE-RSP, which won CEC'2021
competition on single objective bound-constrained numerical optimization for shifted and …

Evolutionary multitask optimization employs similarities among tasks via evolutionary
algorithms (EAs) with knowledge transfer techniques to address multiple optimization tasks …

This study proposes a technique aimed at the automatic search for parameter adaptation
strategies in a differential evolution algorithm with genetic programming symbolic …

Real parameter single objective optimization has been studied for decades. In recent, long-
term search is emphasized based on the consideration that, in the field, solving difficulty …