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Journal ArticleDOI

A novel heuristic optimization method: charged system search

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TLDR
A comparison of the results with those of other evolutionary algorithms shows that the proposed algorithm outperforms its rivals.
Abstract
This paper presents a new optimization algorithm based on some principles from physics and mechanics, which will be called Charged System Search (CSS). We utilize the governing Coulomb law from electrostatics and the Newtonian laws of mechanics. CSS is a multi-agent approach in which each agent is a Charged Particle (CP). CPs can affect each other based on their fitness values and their separation distances. The quantity of the resultant force is determined by using the electrostatics laws and the quality of the movement is determined using Newtonian mechanics laws. CSS can be utilized in all optimization fields; especially it is suitable for non-smooth or non-convex domains. CSS needs neither the gradient information nor the continuity of the search space. The efficiency of the new approach is demonstrated using standard benchmark functions and some well-studied engineering design problems. A comparison of the results with those of other evolutionary algorithms shows that the proposed algorithm outperforms its rivals.

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Citations
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Journal ArticleDOI

Grey Wolf Optimizer

TL;DR: The results of the classical engineering design problems and real application prove that the proposed GWO algorithm is applicable to challenging problems with unknown search spaces.
Journal ArticleDOI

The Whale Optimization Algorithm

TL;DR: Optimization results prove that the WOA algorithm is very competitive compared to the state-of-art meta-heuristic algorithms as well as conventional methods.
Journal ArticleDOI

Salp Swarm Algorithm

TL;DR: The qualitative and quantitative results prove the efficiency of SSA and MSSA and demonstrate the merits of the algorithms proposed in solving real-world problems with difficult and unknown search spaces.
Journal ArticleDOI

Moth-flame optimization algorithm

TL;DR: The MFO algorithm is compared with other well-known nature-inspired algorithms on 29 benchmark and 7 real engineering problems and the statistical results show that this algorithm is able to provide very promising and competitive results.
Journal ArticleDOI

Harris hawks optimization: Algorithm and applications

TL;DR: The statistical results and comparisons show that the HHO algorithm provides very promising and occasionally competitive results compared to well-established metaheuristic techniques.
References
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Book

Genetic algorithms in search, optimization, and machine learning

TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Journal ArticleDOI

Optimization by Simulated Annealing

TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
Book

Adaptation in natural and artificial systems

TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
Book

Genetic Algorithms

Proceedings ArticleDOI

A new optimizer using particle swarm theory

TL;DR: The optimization of nonlinear functions using particle swarm methodology is described and implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm.
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