Journal ArticleDOI
An enhanced chimp optimization algorithm for optimal degree reduction of Said-Ball curves
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In this article , an enhanced chimp optimization algorithm (CHOA) is used to solve the problem of approximate multi-degree reduction of said-ball curve with and without endpoint preserving interpolation.About:
This article is published in Mathematics and Computers in Simulation.The article was published on 2022-02-01. It has received 26 citations till now. The article focuses on the topics: Computer science & Degree (music).read more
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MCSA: Multi-strategy boosted chameleon-inspired optimization algorithm for engineering applications
TL;DR: Wang et al. as discussed by the authors proposed an efficient enhanced chameleon swarm algorithm termed MCSA, combined with fractional-order calculus, sinusoidal adjustment of parameters and crossover-based comprehensive learning (CCL) strategy, which has shown well competitive performance with other state-of-the-art algorithms.
Journal ArticleDOI
Quadratic interpolation boosted black widow spider-inspired optimization algorithm with wavelet mutation
TL;DR: In this article , an enhanced Black Widow Optimization (QIWBWO) algorithm with three improvement strategies is proposed, where the theory of good points set is used to obtain the better initial population, which helps the algorithm to quickly determine the correct search direction.
Journal ArticleDOI
Multi-strategy assisted chaotic coot-inspired optimization algorithm for medical feature selection: A cervical cancer behavior risk study
TL;DR: In this paper , an improved population-initialized COOT algorithm named COBHCOOT is developed by integrating chaos map, opposition-based learning strategy and hunting strategy, which are used to accelerate the global convergence speed and boost the exploration efficiency and solution quality of the algorithm.
Journal ArticleDOI
DTCSMO: An efficient hybrid starling murmuration optimizer for engineering applications
TL;DR: In this paper , an efficient hybrid starling murmuration optimizer that combines dynamic opposition, Taylor-based optimal neighborhood strategy, and crossover operator is developed in order to ameliorate the issues of poor search capability, iterative stagnation, and low convergence accuracy.
Journal ArticleDOI
EJS: Multi-Strategy Enhanced Jellyfish Search Algorithm for Engineering Applications
TL;DR: In this paper , an enhanced jellyfish search (EJS) algorithm is developed, and three improvements are made: (i) by adding a sine and cosine learning factors strategy, the jellyfish can learn from both random individuals and the best individual during Type B motion in the swarm to enhance optimization capability and accelerate convergence speed.
References
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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.
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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.
Proceedings ArticleDOI
Cuckoo Search via Lévy flights
Xin-She Yang,Suash Deb +1 more
TL;DR: A new meta-heuristic algorithm, called Cuckoo Search (CS), is formulated, based on the obligate brood parasitic behaviour of some cuckoo species in combination with the Lévy flight behaviour ofSome birds and fruit flies, for solving optimization problems.
Journal ArticleDOI
On the performance of artificial bee colony (ABC) algorithm
Dervis Karaboga,Bahriye Basturk +1 more
TL;DR: The simulation results show that the performance of ABC algorithm is comparable to those of differential evolution, particle swarm optimization and evolutionary algorithm and can be efficiently employed to solve engineering problems with high dimensionality.
Journal ArticleDOI
SCA: A Sine Cosine Algorithm for solving optimization problems
TL;DR: The SCA algorithm obtains a smooth shape for the airfoil with a very low drag, which demonstrates that this algorithm can highly be effective in solving real problems with constrained and unknown search spaces.