A Novel Hybrid Algorithm Based on Grey Wolf Optimizer and Fireworks Algorithm

Yue, Zhihang; Zhang, Sen; Xiao, Wendong

doi:10.3390/s20072147

Cited by 42 publications

(27 citation statements)

References 30 publications

(19 reference statements)

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“…where a random position vector 𝑋 ⃗⃗⃗ 𝑟𝑎𝑛𝑑 of a random whale is chosen from the current population. Other optimization techniques can be consulted and compared to WAO in the future work of this study (Humaidi et al, 2021;Al-Azza et al, 2015;Moezi et al, 2018;Mirjalili , 2016;Yue et al, 2020).…”

Section: The Search For a Prey (Exploration Phase)mentioning

confidence: 99%

Finite-Time Control of Wing-Rock Motion for Delta Wing Aircraft Based on Whale-Optimization Algorithm

Al-Qassar

Al-Obaidi

Hasan

et al. 2021

Indonesian J. Sci. Technol

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The rise of wing-rock motion in delta-wing aircraft has an adverse effect on the manoeuvrability of aircraft and it may result in its crash. This study presents a finite-time control design to tackle the dynamic motion due to the Wing-Rock effect in delta-wing aircraft. The control design is developed based on the methodology of Super Twisting Sliding Mode Control (STSMC). The Lyapunov stability analysis has been pursued to ensure asymptotic convergence of errors and to determine the finite time. The design of STSMC leads to the appearance of design parameters, which have a direct impact on the dynamic performance of the controlled system. To avoid the conventional tuning of these parameters and to have an optimal performance of the proposed controller, a modern optimization technique has been proposed based on Wale Optimization Algorithm. A comparison study between optimal and non-optimal finite-time super twisting sliding mode controllers has been established and their effectiveness has been verified via numerical simulation using MATLAB programming format.

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Section: The Search For a Prey (Exploration Phase)mentioning

confidence: 99%

Finite-Time Control of Wing-Rock Motion for Delta Wing Aircraft Based on Whale-Optimization Algorithm

Al-Qassar

Al-Obaidi

Hasan

et al. 2021

Indonesian J. Sci. Technol

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“…An extensive technical background based on meta-heuristics has been presented in the literature in recent years, becoming from the most diverse inspirations, such as the well-known evolutionary algorithms of genetic inspiration [ 23 ], the behavior of students in the classroom [ 24 ], the behavior of different species of animals [ 25 ], and some theoretic-mathematical concepts like the golden ratio [ 26 ]. In particular, some of these techniques have been used successfully in the JSSP solution.…”

Section: Related Workmentioning

confidence: 99%

A Modified Genetic Algorithm with Local Search Strategies and Multi-Crossover Operator for Job Shop Scheduling Problem

Viana

Morandin

Contreras

2020

Sensors

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It is not uncommon for today’s problems to fall within the scope of the well-known class of NP-Hard problems. These problems generally do not have an analytical solution, and it is necessary to use meta-heuristics to solve them. The Job Shop Scheduling Problem (JSSP) is one of these problems, and for its solution, techniques based on Genetic Algorithm (GA) form the most common approach used in the literature. However, GAs are easily compromised by premature convergence and can be trapped in a local optima. To address these issues, researchers have been developing new methodologies based on local search schemes and improvements to standard mutation and crossover operators. In this work, we propose a new GA within this line of research. In detail, we generalize the concept of a massive local search operator; we improved the use of a local search strategy in the traditional mutation operator; and we developed a new multi-crossover operator. In this way, all operators of the proposed algorithm have local search functionality beyond their original inspirations and characteristics. Our method is evaluated in three different case studies, comprising 58 instances of literature, which prove the effectiveness of our approach compared to traditional JSSP solution methods.

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“…The obtained results indicate that this combination achieved superior exploration. In [ 31 ], a new memetic combining the exploration ability of the fireworks algorithm (FWA) with the exploitation ability of GWO is proposed. Utilizing 16 benchmark functions with varied dimensions and complexities, the experimental results indicate that the hybrid algorithm attained attractive global search abilities and convergence speeds.…”

Section: Literature Reviewsmentioning

confidence: 99%

“…In this paper, this conversion is achieved by foremost applying squashing of the continuous solutions in each dimension using a sigmoid (S-shaped) transfer function [61]. This will compel the search agents to move into a binary search space as depicted by equation (31).…”

Section: Proposed Eacsidgwo (Binary Version)mentioning

confidence: 99%

An Innovative Excited-ACS-IDGWO Algorithm for Optimal Biomedical Data Feature Selection

Segera

Mbuthia

Nyete

2020

BioMed Research International

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Finding an optimal set of discriminative features is still a crucial but challenging task in biomedical science. The complexity of the task is intensified when any of the two scenarios arise: a highly dimensioned dataset and a small sample-sized dataset. The first scenario poses a big challenge to existing machine learning approaches since the search space for identifying the most relevant feature subset is so diverse to be explored quickly while utilizing minimal computational resources. On the other hand, the second aspect poses a challenge of too few samples to learn from. Though many hybrid metaheuristic approaches (i.e., combining multiple search algorithms) have been proposed in the literature to address these challenges with very attractive performance compared to their counterpart standard standalone metaheuristics, more superior hybrid approaches can be achieved if the individual metaheuristics within the proposed hybrid algorithms are improved prior to the hybridization. Motivated by this, we propose a new hybrid Excited- (E-) Adaptive Cuckoo Search- (ACS-) Intensification Dedicated Grey Wolf Optimization (IDGWO), i.e., EACSIDGWO. EACSIDGWO is an algorithm where the step size of ACS and the nonlinear control strategy of parameter a→ of the IDGWO are innovatively made adaptive via the concept of the complete voltage and current responses of a direct current (DC) excited resistor-capacitor (RC) circuit. Since the population has a higher diversity at early stages of the proposed EACSIDGWO algorithm, both the ACS and IDGWO are jointly involved in local exploitation. On the other hand, to enhance mature convergence at latter stages of the proposed algorithm, the role of ACS is switched to global exploration while the IDGWO is still left conducting the local exploitation. To prove that the proposed algorithm is superior in providing a good learning from fewer instances and an optimal feature selection from information-rich biomedical data, all these while maintaining a high classification accuracy of the data, the EACSIDGWO is employed to solve the feature selection problem. The EACSIDGWO as a feature selector is tested on six standard biomedical datasets from the University of California at Irvine (UCI) repository. The experimental results are compared with the state-of-the-art feature selection techniques, including binary ant-colony optimization (BACO), binary genetic algorithm (BGA), binary particle swarm optimization (BPSO), and extended binary cuckoo search algorithm (EBCSA). These results reveal that the EACSIDGWO has comprehensive superiority in tackling the feature selection problem, which proves the capability of the proposed algorithm in solving real-world complex problems. Furthermore, the superiority of the proposed algorithm is proved via various numerical techniques like ranking methods and statistical analysis.

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A Novel Hybrid Algorithm Based on Grey Wolf Optimizer and Fireworks Algorithm

Cited by 42 publications

References 30 publications

Finite-Time Control of Wing-Rock Motion for Delta Wing Aircraft Based on Whale-Optimization Algorithm

Finite-Time Control of Wing-Rock Motion for Delta Wing Aircraft Based on Whale-Optimization Algorithm

A Modified Genetic Algorithm with Local Search Strategies and Multi-Crossover Operator for Job Shop Scheduling Problem

An Innovative Excited-ACS-IDGWO Algorithm for Optimal Biomedical Data Feature Selection

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