Nature inspired meta heuristic algorithms for optimization problems

S, Vinod Chandra S; Anand, H. S.

doi:10.1007/s00607-021-00955-5

Cited by 72 publications

(20 citation statements)

References 54 publications

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“…Heuristic approaches employ empirical methods or approximations that do not guarantee an optimal general solution but are sufficient to achieve approximate solutions for given specific problems. This technique is satisfactory in a limited time frame and can also significantly speed up the optimization process [29].…”

Section: Metaheuristic Methodsmentioning

confidence: 95%

A Hybrid Service Selection and Composition for Cloud Computing Using the Adaptive Penalty Function in Genetic and Artificial Bee Colony Algorithm

Sefati

Halunga

2022

Sensors

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The rapid development of Cloud Computing (CC) has led to the release of many services in the cloud environment. Service composition awareness of Quality of Service (QoS) is a significant challenge in CC. A single service in the cloud environment cannot respond to the complex requests and diverse requirements of the real world. In some cases, one service cannot fulfill the user’s needs, so it is necessary to combine different services to meet these requirements. Many available services provide an enormous QoS and selecting or composing those combined services is called an Np-hard optimization problem. One of the significant challenges in CC is integrating existing services to meet the intricate necessities of different types of users. Due to NP-hard complexity of service composition, many metaheuristic algorithms have been used so far. This article presents the Artificial Bee Colony and Genetic Algorithm (ABCGA) as a metaheuristic algorithm to achieve the desired goals. If the fitness function of the services selected by the Genetic Algorithm (GA) is suitable, a set of services is further introduced for the Artificial Bee Colony (ABC) algorithm to choose the appropriate service from, according to each user’s needs. The proposed solution is evaluated through experiments using Cloud SIM simulation, and the numerical results prove the efficiency of the proposed method with respect to reliability, availability, and cost.

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Section: Metaheuristic Methodsmentioning

confidence: 95%

A Hybrid Service Selection and Composition for Cloud Computing Using the Adaptive Penalty Function in Genetic and Artificial Bee Colony Algorithm

Sefati

Halunga

2022

Sensors

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“…The benefits of this strategy are summarized as follows [39–41]: Dimensionality reduction: By avoiding the high dimensionality of precise approaches, the ARMA model makes the approach more computationally practical. Flexibility: Metaheuristic algorithms, such as AVOA, provide flexibility in the solution of difficult optimization problems. The merging of ARMA and AVOA enables a flexible and customizable solution strategy that caters to specific problem requirements. Consideration of temporal dependencies: The ARMA model identifies patterns and temporal dependencies in wind power by analyzing past data, resulting in more accurate projections.…”

Section: Wind Power Integrationmentioning

confidence: 99%

Solving wind‐integrated unit commitment problem by a modified African vultures optimization algorithm

Abuelrub,

Awwad,

Al‐Masri

2023

IET Generation Trans & Dist

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Unit commitment (UC) stands out as a significant challenge in electrical power systems. With the rapid growth in power demand and the pressing issues of fossil fuel scarcity and global warming, it has become crucial to enhance the utilization of renewable energy sources. This study focuses on addressing the UC problem by incorporating a wind farm and proposes a modified version of the metaheuristic African vultures optimization algorithm (AVOA) in binary form, utilizing the sigmoid transfer function. The modified AVOA employs multiple phase‐shift tactics to overcome premature local optima. By determining the on/off status of generating units, the modified AVOA improves the algorithm's effectiveness. Additionally, the paper develops an auto‐regressive moving average model (ARMA) to forecast wind speeds, with the AVOA assisting in selecting the optimal orders (q and p) of the ARMA model. This is done using historical wind speed data to capture uncertainty in the wind speed. The wind power is then calculated using various models and integrated into the UC problem. The effectiveness of the modified AVOA is examined on the IEEE 30‐bus system. The binary AVOA (BAVOA) outperforms several algorithms presented in the case study, demonstrating its superiority. Furthermore, the results indicate that BAVOA delivers superior outcomes within the discrete search space when compared to the continuous search space.

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“…Fortunately, metaheuristic algorithms appeared in the 1980s [9], opened up promising new perspectives thanks to their strengths for solving NP-Hard problems [10] particularly for discrete problems where we are confronted with the explosion combinatorial. These algorithms, mostly inspired from the nature, mimic the human behavior, physics, biology and animal swarm intelligence [11][12][13][14][15][16][17][18][19][20][21]. The qualities of metaheuristics are very well defined by Osman, I.H.…”

Section: Introductionmentioning

confidence: 99%

BHHO-EAS metaheuristic applied to the NP-Hard wrapper feature selection multi-objective optimization problem

SASSI,

CHELOUAH

2024

Preprint

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Faced with the increase in high-dimensional Big Data creating more volume and complexity, the feature selection process became an essential phase in the preprocessing workflow upstream of the design of systems based on deep learning. This paper is a concrete and first application of the new metaheuristic Harris Hawk Optimization Encirclement-Attack-Synergy (HHO-EAS) in solving the NP-Hard wrapper feature selection multi-objective optimization problem. This problem combines two contradictory objectives: maximizing the accuracy of a classifier while minimizing the number of the most relevant and non-redundant selected features. To do this we hybridized HHO-EAS to create the new metaheuristic Binary HHO-EAS (BHHO-EAS). We combined HHO-EAS to the sixteen transfer functions most used in the literature structured in a balanced way among the four main categories including S-Shaped, V-Shaped, Q-Shaped and U-Shaped. This wide range of transfer function allows us to analyze the evolution of BHHO-EAS’s skills according to the assigned transfer function and to determine which of them offer the best performances. We applied wrapper feature selection to the well-known NSL-KDD dataset with the deep learning Multi Layer Perceptron (MLP) classifier. We put BHHO-EAS in competition with three other well-known population based binary metaheuristics, BPSO, BBA and BHHO. The analysis of the experimental results, compared to the three other binary metaheuristics, demonstrated that BHHO-EAS obtained the best performance on 100% of the transfer functions. This is more particularly highlighted by the U-Shaped transfer functions, which give an acceptable compromise for the two objectives of the problem with an average accuracy of 96,4% and an average size of selected features of 20.

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Nature inspired meta heuristic algorithms for optimization problems

Cited by 72 publications

References 54 publications

A Hybrid Service Selection and Composition for Cloud Computing Using the Adaptive Penalty Function in Genetic and Artificial Bee Colony Algorithm

A Hybrid Service Selection and Composition for Cloud Computing Using the Adaptive Penalty Function in Genetic and Artificial Bee Colony Algorithm

Solving wind‐integrated unit commitment problem by a modified African vultures optimization algorithm

BHHO-EAS metaheuristic applied to the NP-Hard wrapper feature selection multi-objective optimization problem

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