Intelligent facial emotion recognition based on Hybrid whale optimization algorithm and sine cosine algorithm

Vijayalakshmi, A.; Mohanaiah, P.

doi:10.1016/j.micpro.2022.104718

Cited by 7 publications

(3 citation statements)

References 44 publications

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“…The results show that the WOASCALF benefits SCA in terms of exploitation ability and profits of Lévy flight strategy in terms of population diversity by providing small and large jumps in different directions of the search space. Lakshmi et al [ 104 ] proposed WOA-SCA by hybridization of SCA and WOA to solve the facial emotion recognition problem. Results show that WOA-SCA can improve the exploration strategy of the WOA effectively and obtain average emotion recognition accuracy equal to 98%.…”

Section: Hybrid Variants Of Woamentioning

confidence: 99%

A Systematic Review of the Whale Optimization Algorithm: Theoretical Foundation, Improvements, and Hybridizations

Nadimi-Shahraki

Zamani

Varzaneh

et al. 2023

Arch Computat Methods Eng

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Despite the simplicity of the whale optimization algorithm (WOA) and its success in solving some optimization problems, it faces many issues. Thus, WOA has attracted scholars' attention, and researchers frequently prefer to employ and improve it to address real-world application optimization problems. As a result, many WOA variations have been developed, usually using two main approaches improvement and hybridization. However, no comprehensive study critically reviews and analyzes WOA and its variants to find effective techniques and algorithms and develop more successful variants. Therefore, in this paper, first, the WOA is critically analyzed, then the last 5 years' developments of WOA are systematically reviewed. To do this, a new adapted PRISMA methodology is introduced to select eligible papers, including three main stages: identification, evaluation, and reporting. The evaluation stage was improved using three screening steps and strict inclusion criteria to select a reasonable number of eligible papers. Ultimately, 59 improved WOA and 57 hybrid WOA variants published by reputable publishers, including Springer, Elsevier, and IEEE, were selected as eligible papers. Effective techniques for improving and successful algorithms for hybridizing eligible WOA variants are described. The eligible WOA are reviewed in continuous, binary, single-objective, and multi/many-objective categories. The distribution of eligible WOA variants regarding their publisher, journal, application, and authors' country was visualized. It is also concluded that most papers in this area lack a comprehensive comparison with previous WOA variants and are usually compared only with other algorithms. Finally, some future directions are suggested.

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Section: Hybrid Variants Of Woamentioning

confidence: 99%

A Systematic Review of the Whale Optimization Algorithm: Theoretical Foundation, Improvements, and Hybridizations

Nadimi-Shahraki

Zamani

Varzaneh

et al. 2023

Arch Computat Methods Eng

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“…This algorithm has been widely applied across various research domains. It has been utilized for image segmentation [124], in the validation of welded Al/Cu bimetal sheets [125], for intelligent facial emotion recognition [126], to enhance power system stabilizers [127], and in task scheduling for microprocessor systems [128].…”

Section: Whale Optimization Algorithm (Woa)mentioning

confidence: 99%

Optimizing Path Planning for Smart Vehicles: A Comprehensive Review of Metaheuristic Algorithms

Mbah,

Zeeshan

2023

JEMSE

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In the realm of smart vehicle navigation, both in known and unknown environments, the crucial aspects encompass the vehicle's localization using an array of technologies such as GPS, cameras, vision systems, laser, and ultrasonic sensors. This process is pivotal for effective motion planning within the vehicle's free configuration space, enabling it to adeptly avoid obstacles. The focal point of such navigation systems lies in devising a path from an initial to a target configuration, striving to minimize the path length and the time taken, while simultaneously circumventing obstacles. The application of metaheuristic algorithms has been pivotal in this regard. These algorithms, characterized by their ability to exploit initial solutions and explore the environment for feasible pathways, have been extensively utilized. A significant body of research in robotics and automation has focused on evaluating the efficacy of population-based algorithms including Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Firefly Algorithm (FA), and Whale Optimization Algorithm (WOA). Additionally, trajectory-based methods such as Tabu Search (TS) and Simulated Annealing (SA) have been scrutinized for their proficiency in identifying short, feasible paths among the plethora of solutions. There has been a surge in the enhancement and modification of these algorithms, with a multitude of hybrid metaheuristic algorithms being proposed. This review meticulously examines various metaheuristic algorithms and their hybridizations, specifically in their application to the path planning challenges faced by smart vehicles. The exploration extends to the comparison of these algorithms, highlighting their distinct advantages and limitations. Furthermore, the review delves into potential future directions in this evolving field, emphasizing the continual refinement of these algorithms to cater to the increasingly complex demands of smart vehicle navigation.

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“…This algorithm offers advantages such as simplicity in principles, fewer parameters, and ease of implementation. It has successfully been applied to solve a variety of problems in fields such as image retrieval 24 , image segmentation 25 , medicine 26 , energy 27 , neural networks 28 , feature selection 29 , wind speed prediction 30 , key recognition 31 , and sentiment analysis 32 , among others. However, WOA still faces challenges when applied to nonlinear, high-dimensional, and complex optimization problems, including issues related to low optimization precision, slow convergence, and susceptibility to local convergence.…”

Section: Introductionmentioning

confidence: 99%

A whale optimization algorithm based on atom-like structure differential evolution for solving engineering design problems

Tang,

Wang

2024

Sci Rep

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The whale optimization algorithm has received much attention since its introduction due to its outstanding performance. However, like other algorithms, the whale optimization algorithm still suffers from some classical problems. To address the issues of slow convergence, low optimization precision, and susceptibility to local convergence in the whale optimization algorithm (WOA). Defining the optimization behavior of whale individuals as quantum mechanical behavior, a whale optimization algorithm based on atom-like structure differential evolution (WOAAD) is proposed. Enhancing the spiral update mechanism by introducing a sine strategy guided by the electron orbital center. Improving the random-walk foraging mechanism by applying mutation operations to both the electron orbital center and random individuals. Performing crossover operations between the newly generated individuals from the improved mechanisms and random dimensions, followed by a selection process to retain superior individuals. This accelerates algorithm convergence, enhances optimization precision, and prevents the algorithm from falling into local convergence. Finally, implementing a scouting bee strategy, where whale individuals progressively increase the number of optimization failures within a limited parameter L. When a threshold is reached, random initialization is carried out to enhance population diversity. Conducting simulation experiments to compare the improved algorithm with the whale optimization algorithm, other optimization algorithms, and other enhanced whale optimization algorithms. The experimental results indicate that the improved algorithm significantly accelerates convergence, enhances optimization precision, and prevents the algorithm from falling into local convergence. Applying the improved algorithm to five engineering design problems, the experimental results demonstrate that the improved algorithm exhibits good applicability.

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Intelligent facial emotion recognition based on Hybrid whale optimization algorithm and sine cosine algorithm

Cited by 7 publications

References 44 publications

A Systematic Review of the Whale Optimization Algorithm: Theoretical Foundation, Improvements, and Hybridizations

A Systematic Review of the Whale Optimization Algorithm: Theoretical Foundation, Improvements, and Hybridizations

Optimizing Path Planning for Smart Vehicles: A Comprehensive Review of Metaheuristic Algorithms

A whale optimization algorithm based on atom-like structure differential evolution for solving engineering design problems

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