Many problems in real life can be seen as Expensive Optimization Problems (EOPs).
Compared with traditional optimization problems, the evaluation cost of candidate solutions …

Abstract Generally, Synthetic Benchmark Problems (SBPs) are utilized to assess the
performance of metaheuristics. However, these SBPs may include various unrealistic …

It is witnessed that the popularity of the research in cybersecurity using bio-inspired
algorithms (a key subset of natural algorithms) is ever-growing. As an emergent research …

Forecasting plays a vital role in solar power generation and skillfully managing renewable
energy resources. The traditional artificial neural network (ANN) has certain limitations with …

This study builds a two-level energy management strategy framework for decentralized
autonomy of microgrids and optimal coordinated operation of a multi-microgrid system. To …

Over the past decade, incorporating information from the objective function into the
constraint-handling process has garnered considerable attention in evolutionary algorithm …

Power flow (PF) analysis of microgrids (MGs) has been gaining a lot of attention due to the
evolution of islanded MGs. To calculate islanded MGs' PF solution, a globally convergent …

Differential evolution (DE) has been a simple yet effective algorithm for global optimization
problems. The performance of DE highly depends on its operators and parameter settings …

The mathematical form of many optimization problems in engineering is constrained
optimization problems. In this paper, an improved genetic algorithm based on two-direction …

A two-phase constraint-handling technique is integrated into the evolutionary algorithms to
solve constrained optimization problems (called TPDE) in this article. In phase one, denoted …