Archimedes Optimizer: Theory, Analysis, Improvements, and Applications

Abstract

The intricacy of the real-world numerical optimization tribulations has full-fledged and diversely amplified necessitating proficient yet ingenious optimization algorithms. In the domain wherein the classical approaches fall short, the predicament resolving nature-inspired optimization algorithms (NIOA) tend to hit upon an excellent solution to unbendable optimization problems consuming sensible computation time. Nevertheless, in the last few years approaches anchored in nonlinear physics have been anticipated, announced, and flourished. The process based on non-linear physics modeled in the form of optimization algorithms and as a subset of NIOA, in countless cases, has successfully surpassed the existing optimization methods with their effectual exploration knack thus formulating utterly fresh search practices. Archimedes Optimization Algorithm (AOA) is one of the recent and most promising physics optimization algorithms that use meta-heuristics phenomenon to solve real-world problems by either maximizing or minimizing a variety of measurable variables such as performance, profit, and quality. In this paper, Archimedes Optimization Algorithm (AOA) has been discussed in great detail, and also its performance was examined for Multi-Level Thresholding (MLT) based image segmentation domain by considering t-entropy and Tsallis entropy as objective functions. The experimental results showed that among recent Physics Inspired Optimization Algorithms (PIOA), the Archimedes Optimization Algorithm (AOA) produces very promising outcomes with Tsallis entropy rather than with t-entropy in both color standard images and medical pathology images.

A Comprehensive Survey on Arithmetic Optimization Algorithm

“…x i,j (t + 1) = best x j ÷ (MoPr + 𝜖) × U j − L j × 𝜇 + L j , rand2 < 0.5 best x j × (MoPr) × U j − L j × 𝜇 + L j , Otherwise (4) In AOA, the exploitation strategy has been developed using Subtraction (S) or Addition (A) operators as formulated in Eq. (5). Here is also a constant which is fixed equal to 0.5 in the source paper.…”

“…Many NIOAs have been created in the present that mimic the characteristics of biological and natural systems [3]. These algorithms are extremely robust and effective at resolving practical optimization issues in a reasonable amount of time [3][4][5]. Creating these sophisticated algorithms has as its goal the generation of novel and effective answers to the challenging optimization problems that arise in the actual world.…”

“…In addition to that, the literature reports various NIOAs that have been produced up to the year 2022 that are based not just on natural laws but also on physical, social, and biological aspects. Under such circumstances, literature [1,3] has classified NIOAs into some heterogeneous classes (a) Evolutionary Algorithms (EA) [1,2]; (b) Bio-inspired optimization algorithms (BIOA) [1,2]; (c) Physics inspired optimization algorithms (PIOA) [5]; (d) Chemistry inspired optimization algorithms (CIOA) [4]; (e) Mathematics inspired optimization algorithms (MIOA) [4]; (f) Human inspired optimization algorithms (HIOA) [6]. The classification of NIOAs is represented as Fig.…”

Recent Developments in Equilibrium Optimizer Algorithm: Its Variants and Applications

“…4.2, many swarm-based [239] and human-based meta-heuristic algorithms [240] have been combined with EO for different types of optimization problems. Further applications or integrations of other NIOA algorithm [241] types are possible, such as Plant-based, Maths-based or even Physics-Chemistrybased meta-heuristic algorithms [242,243], to determine the EO's potential, improve computational performance, and produce good solutions. 4.…”

Storage Optimization (r, Q) Strategy under Condition-Based Maintenance of Key Equipment of Coal-Fired Power Units in Carbon Neutrality Era

“…The goal of optimization is to find the best solution to a problem. The metaheuristic algorithm is one of the most powerful tools for solving optimization problems [21][22][23][24][25].…”