One of the main issues with heuristics and meta-heuristics is the local optima stagnation
phenomena. It is often called premature convergence, which refers to the assumption of a …

Metaheuristic algorithms are important methods to solve optimization problems and
maintaining a balance between the global exploration and local exploitation is crucial to the …

The growth in energy consumption and pollution has led to increase the utilization of clean
energy resources and electric vehicles (EVs), making the issue of network uncertainty a …

This paper proposes a grid-guided particle swarm optimizer for solving multimodal multi-
objective optimization problems that may have multiple disjoint Pareto sets corresponding to …

Online diagnosis for sucker rod pumping well has great significances for rapidly grasping
operations of the oil well. Feature extraction of the working condition and determination of …

The poor comprehensiveness of the evaluation indexes of quality evaluation methods for the
traditional college mathematics teaching model reform results in low accuracy of the …

This paper presents a new hybrid genetic-particle swarm optimization (GPSO) algorithm for
solving multi-constrained optimization problems. This algorithm is different from the …

Abstract The Internet of Things (IoT) is an emerging domain in recent days as they provided
a huge number of applications in day‐to‐day lives. In contrast to the agricultural sector, the …

Optimal operation in islanded microgrids is one of most fundamental and prominent non-
linear optimization problems investigated with new/different objective (s) with respect to the …

In this paper, an improved neural network enterprise credit rating model, which is grounded
on a genetic algorithm, is suggested. With the characteristics of self‐adaptiveness and self …