MAKHA—A New Hybrid Swarm Intelligence Global Optimization Algorithm

Khalil, Ahmed M.E.; Fateen, Seif-Eddeen K.; Bonilla-Petriciolet, Adrián

doi:10.3390/a8020336

Cited by 11 publications

(4 citation statements)

References 35 publications

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“…Another swarm intelligence optimizer that found itself being combined with another algorithm was the krill herd (KH) algorithm. Again, KH often got trapped in a local optimum and Ahmed M. E. Khalil, Seif-Eddeen K. Fateen, and Adrián Bonilla-Petriciolet had to improve it by combining it with a Monkey Algorithm (MA) [25]. The cuckoo search (CS) algorithm was rather popular, but, as discussed by Kamel Zeltni and Souham Meshoul, was only suited for unconstrained multi-objective problems [26].…”

Section: Optimization Methods Used In Wsp Accuracy Enhancementmentioning

confidence: 99%

Prediction of Wind Speed Using a Hybrid Regression-Optimization Approach

Ramachandran¹,

Swaminathan²

2023

JPEE

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Predicting wind speed is a complex task that involves analyzing various meteorological factors such as temperature, humidity, atmospheric pressure, and topography. There are different approaches that can be used to predict wind speed, and a hybrid optimization approach is one of them. In this paper, the hybrid optimization approach combines a multiple linear regression approach with an optimization technique to achieve better results. In the context of wind speed prediction, this hybrid optimization approach can be used to improve the accuracy of existing prediction models. Here, a Grey Wolf Optimizer based Wind Speed Prediction (GWO-WSP) method is proposed. This approach is tested on the 2016, 2017, 2018, and 2019 Raw Data files from the Great Lakes Environmental Research Laboratories and the National Oceanic and Atmospheric Administration's (GLERL-NOAA) Chicago Metadata Archive. The test results show that the implementation is successful and the approach yields accurate and feasible results. The computation time for execution of the algorithm is also superior compared to the existing methods in literature.

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Section: Optimization Methods Used In Wsp Accuracy Enhancementmentioning

confidence: 99%

Prediction of Wind Speed Using a Hybrid Regression-Optimization Approach

Ramachandran¹,

Swaminathan²

2023

JPEE

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“…ABC [35] is an optimization algorithm based on the intelligent foraging behavior of honey bee swarm. MAKHA [36] is a hybrid between Monkey Algorithm, which is inspired by the simulation of the climbing processes of monkeys to find the highest mountaintop, and Krill-Herd Algorithm that is based on the simulation of the herding behavior of krill individuals. CMAES [37] is a stochastic and derivative free method for numerical optimization of non-linear non-convex problems.…”

Section: Description Of Nature-inspired Metaheuristics Used For Critimentioning

confidence: 99%

“…In particular, seven of the most promising and most recent nature-inspired optimization methods have been studied in critical point calculations. These algorithms are: Bare Bones Particle Swarm Optimization (BBPSO) [27], Cuckoo Search (CS) [33], Intelligent Firefly (IFA) [34], Artificial Bee Colony (ABC) [35], Monkey and Krill Herd Hybrid (MAKHA) [36], Covariance Matrix Adaptation Evolution Strategy (CMAES) [37] and Flower Pollination Algorithm (FPA) [38]. Performance of these methods have been systematically analyzed using difficult critical point problems, specifically, multicomponent petroleum reservoir fluids from real oil fields with 50 components.…”

Section: Introductionmentioning

confidence: 99%

Critical point calculations of multi-component reservoir fluids using nature-inspired metaheuristic algorithms

Shehata

Fateen

Bonilla-Petriciolet

2016

Fluid Phase Equilibria

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“…This is not the case with animals. There is not much difference between two monkeys in the monkey algorithm [29] or between two wolves in the wolf pack algorithm [7]. However, every human has a different set of skills and capabilities.…”

Section: Nature-inspired Algorithm Commentsmentioning

confidence: 99%

SPGD: Search Party Gradient Descent Algorithm, a Simple Gradient-Based Parallel Algorithm for Bound-Constrained Optimization

Hameed¹,

Rajagopalan²

2022

Mathematics

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Nature-inspired metaheuristic algorithms remain a strong trend in optimization. Human-inspired optimization algorithms should be more intuitive and relatable. This paper proposes a novel optimization algorithm inspired by a human search party. We hypothesize the behavioral model of a search party searching for a treasure. Motivated by the search party’s behavior, we abstract the “Divide, Conquer, Assemble” (DCA) approach. The DCA approach allows us to parallelize the traditional gradient descent algorithm in a strikingly simple manner. Essentially, multiple gradient descent instances with different learning rates are run parallelly, periodically sharing information. We call it the search party gradient descent (SPGD) algorithm. Experiments performed on a diverse set of classical benchmark functions show that our algorithm is good at optimizing. We believe our algorithm’s apparent lack of complexity will equip researchers to solve problems efficiently. We compare the proposed algorithm with SciPy’s optimize library and it is found to be competent with it.

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MAKHA—A New Hybrid Swarm Intelligence Global Optimization Algorithm

Cited by 11 publications

References 35 publications

Prediction of Wind Speed Using a Hybrid Regression-Optimization Approach

Prediction of Wind Speed Using a Hybrid Regression-Optimization Approach

Critical point calculations of multi-component reservoir fluids using nature-inspired metaheuristic algorithms

SPGD: Search Party Gradient Descent Algorithm, a Simple Gradient-Based Parallel Algorithm for Bound-Constrained Optimization

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