Journal ArticleDOI
Slime mould algorithm: A new method for stochastic optimization
Shimin Li,Huiling Chen,Mingjing Wang,Ali Asghar Heidari,Ali Asghar Heidari,Seyedali Mirjalili +5 more
TLDR
The proposed slime mould algorithm has several new features with a unique mathematical model that uses adaptive weights to simulate the process of producing positive and negative feedback of the propagation wave of slime mould based on bio-oscillator to form the optimal path for connecting food with excellent exploratory ability and exploitation propensity.About:
This article is published in Future Generation Computer Systems.The article was published on 2020-10-01. It has received 1443 citations till now. The article focuses on the topics: SMA* & Stochastic optimization.read more
Citations
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Hunger games search: Visions, conception, implementation, deep analysis, perspectives, and towards performance shifts
TL;DR: This open-source population-based optimization technique called Hunger Games Search is designed to be a standard tool for optimization in different areas of artificial intelligence and machine learning with several new exploratory and exploitative features, high performance, and high optimization capacity.
Book
Knowledge-Based Systems
TL;DR: This text is designed to develop an appreciation of KBS and their architecture and to help users understand a broad variety of knowledge based techniques for decision support and planning.
Journal ArticleDOI
RUN beyond the metaphor: An efficient optimization algorithm based on Runge Kutta method
Iman Ahmadianfar,Ali Asghar Heidari,Ali Asghar Heidari,Amir H. Gandomi,Xuefeng Chu,Huiling Chen +5 more
TL;DR: This study attempts to go beyond the traps of metaphors and introduce a novel metaphor-free population-based optimization based on the mathematical foundations and ideas of the Runge Kutta (RK) method widely well-known in mathematics.
Journal ArticleDOI
Dwarf Mongoose Optimization Algorithm
TL;DR: In this article , a metaheuristic algorithm called dwarf mongoose optimization algorithm (DMO) is proposed to solve the classical and CEC 2020 benchmark functions and 12 continuous/discrete engineering optimization problems.
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The Colony Predation Algorithm
TL;DR: The Colony Predation Algorithm (CPA) as mentioned in this paper is based on the corporate predation of animals in nature and utilizes a mathematical mapping following the strategies used by animal hunting groups, such as dispersing prey, encircling prey, supporting the most likely successful hunter, and seeking another target.
References
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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.
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Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces
Rainer Storn,Kenneth Price +1 more
TL;DR: In this article, a new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented, which requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.
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A Simple Sequentially Rejective Multiple Test Procedure
TL;DR: In this paper, a simple and widely accepted multiple test procedure of the sequentially rejective type is presented, i.e. hypotheses are rejected one at a time until no further rejections can be done.
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No free lunch theorems for optimization
TL;DR: A framework is developed to explore the connection between effective optimization algorithms and the problems they are solving and a number of "no free lunch" (NFL) theorems are presented which establish that for any algorithm, any elevated performance over one class of problems is offset by performance over another class.
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.