To improve the optimization performance of LSHADE algorithm, an alternative adaptation
approach for the selection of control parameters is proposed. The proposed algorithm …

Abstract “Dimensionality” is one of the major problems which affect the quality of learning
process in most of the machine learning and data mining tasks. Having high dimensional …

The initiative to introduce new benchmark problems has drawn attention to the development
of new optimization algorithms. Recently, a set of constrained benchmark problems has …

The existence of the curse of dimensionality is well known, and its general effects are well
acknowledged. However, and perhaps due to this colloquial understanding, specific …

The goal of exploration to produce diverse search points throughout the search space can
be countered by the goal of selection to focus search around the fittest current solution (s). In …

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 …

Differential evolution (DE) is a simple yet powerful evolutionary algorithm (EA) for global
numerical optimization. However, its performance is significantly influenced by its …

Adaptive guided differential evolution algorithm (AGDE) is a differential evolution (DE)
algorithm that utilizes the information of good and bad vectors in the population, it introduced …

Choosing automatically the right algorithm using problem descriptors is a classical
component of combinatorial optimization. It is also a good tool for making evolutionary …

The search capability of differential evolution (DE) is largely affected by control parameters,
mutation and crossover strategies. Therefore, choosing appropriate strategies and control …