A Multi-Strategy Whale Optimization Algorithm and Its Application
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TL;DR: Wang et al. as mentioned in this paper proposed a Multi-Strategy Whale Optimization Algorithm (MSWOA) for solving complex engineering optimization problems, aiming at adjusting important parameters to satisfy constraints and optimal objectives.
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About: This article is published in Engineering Applications of Artificial Intelligence. The article was published on 01 Feb 2022. and is currently open access. The article focuses on the topics: Computer science & Engineering optimization.
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Citations
Opposition-based learning equilibrium optimizer with Levy flight and evolutionary population dynamics for high-dimensional global optimization problems
TL;DR: In this paper , the authors proposed an opposition-based learning EO algorithm with the Levy flight and the evolutionary population dynamics for high-dimensional global optimization problems, called EOOBLE.
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Multistrategy Improved Whale Optimization Algorithm and Its Application
Lisang Liu,Rongsheng Zhang +1 more
TL;DR: A differential evolution chaotic whale optimization algorithm (DECWOA) is proposed, which has a better global optimization-seeking capability and the initial population is generated by introducing the Sine chaos theory at the beginning of the algorithm.
The research of a novel WOG-YOLO algorithm for autonomous driving object detection
Lingzhi Xu,Wei Yan,Jiashu Ji +2 more
TL;DR: In this article , a modified whale optimization algorithm (MWOA) was proposed to enhance the performance of the YOLOv5 model, which leverages the population's concentration ratio to calculate the hunting branch of GWO or WOA.
A modified whale optimization algorithm with multi-strategy mechanism for global optimization problems
TL;DR: A Modified Whale Optimization Algorithm with multi-strategy mechanism, which introduces the elite reverse learning strategy, nonlinear convergence factor, DE/rand/1 mutation strategy and Lévy flight disturbance strategy to improve the efficiency of WOA.
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An adaptive dynamic multi-swarm particle swarm optimization with stagnation detection and spatial exclusion for solving continuous optimization problems
Xu Yang,Hongru Li,Youhe Huang +2 more
TL;DR: Zhang et al. as mentioned in this paper proposed an adaptive dynamic multi-swarm particle swarm optimization with stagnation detection and spatial exclusion (ADPSO) to solve the problems of PSO, and the results showed that the proposed ADPSO has better results compared to the state-of-the-art PSO variants.
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References
Particle swarm optimization
TL;DR: A snapshot of particle swarming from the authors’ perspective, including variations in the algorithm, current and ongoing research, applications and open problems, is included.
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.
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The Whale Optimization Algorithm
Seyedali Mirjalili,Andrew Lewis +1 more
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.
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Self-organized criticality: An explanation of the 1/ f noise
TL;DR: It is shown that dynamical systems with spatial degrees of freedom naturally evolve into a self-organized critical point, and flicker noise, or 1/f noise, can be identified with the dynamics of the critical state.
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A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm
Dervis Karaboga,Bahriye Basturk +1 more
TL;DR: Artificial Bee Colony (ABC) Algorithm is an optimization algorithm based on the intelligent behaviour of honey bee swarm that is used for optimizing multivariable functions and the results showed that ABC outperforms the other algorithms.