An Improved Equilibrium Optimizer with a Decreasing Equilibrium Pool

Abstract

Big Data is impacting and changing the way we live, and its core lies in the use of machine learning to extract valuable information from huge amounts of data. Optimization problems are a common problem in many steps of machine learning. In the face of complex optimization problems, evolutionary computation has shown advantages over traditional methods. Therefore, many researchers are working on improving the performance of algorithms for solving various optimization problems in machine learning. The equilibrium optimizer (EO) is a member of evolutionary computation and is inspired by the mass balance model in environmental engineering. Using particles and their concentrations as search agents, it simulates the process of finding equilibrium states for optimization. In this paper, we propose an improved equilibrium optimizer (IEO) based on a decreasing equilibrium pool. IEO provides more sources of information for particle updates and maintains a higher population diversity. It can discard some exploration in later stages to enhance exploitation, thus achieving a better search balance. The performance of IEO is verified using 29 benchmark functions from IEEE CEC2017, a dynamic economic dispatch problem, a spacecraft trajectory optimization problem, and an artificial neural network model training problem. In addition, the changes in population diversity and computational complexity brought by the proposed method are analyzed.

Recent Developments in Equilibrium Optimizer Algorithm: Its Variants and Applications

“…Further, the experimental results clearly highlight that proposed I-EO has superior performance when compared with AGDE, GWO, MFO, SCA, HHO, TSA and EO for COVID-19 CT images segmentation. Improved Equilibrium Optimizer (IEO), an updated version of EO that especially focuses on population variety maintenance mechanisms to balance the exploration and exploitation in regular EO, was proposed by Yang et al in the year 2022 [210]. Additionally, the experimental findings unmistakably show that the proposed IEO outperforms EO, GGSA, HGSA, and RGBSO for the problems of economic dispatch, spacecraft trajectory optimization, and artificial neural network model training.…”

An Enhanced Equilibrium Optimizer for Solving Optimization Tasks

“…To thoroughly assess the performance of SCEO, this paper compares the SCEO algorithm with five novel EO variants, including the basic EO [9], LWMEO [15], m-EO [16], IS-EO [21], and IEO [17]. To ensure equitable evaluation, a consistent population scale of 30 and a uniform maximum iteration count of 500 are maintained across all tests.…”

“…Through extensive testing on 33 benchmark problems and comparisons to the EO and other advanced approaches, the m-EO demonstrates its superiority over competitors. In [17], an enhanced iteration of EO (IEO) is introduced, incorporating a declining equilibrium pool strategy. The evaluation of IEO encompasses 29 benchmark functions, along with three engineering task, highlighting its versatility and efficacy.…”