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The Whale Optimization Algorithm (WOA) is a new advanced algorithm that is based on the humpback whale chasing process. The main problem addressed by Whale Optimization Algorithm, similar to other metaheuristic algorithms, is premature convergence. This paper introduces the Sine Cosine Algorithm in the Whale Optimization Algorithm optimization process to enhance global convergence and to improve enactment. The proposed algorithm, called WOA-SCA, has local optima evasion of Whale Optimization Algorithm and high capability to increase the exploration. The implementation of the proposed WOA-SCA algorithm is investigated through a series of tests on common benchmark test functions, with the results compared to those of six alternative algorithms. The results of the investigations show that the proposed WOA-SCA algorithm works better in concert with the outputs of the benchmark functions. Also, to validate the proficiencies of the WOA-SCA, it has been used to resolve the real-world problem of Facial Emotion Recognition (FER). The field of facial emotion recognition is a fascinating new area of research that gives us the ability to identify the human face's expression in natural settings. A large part of the standard methodologies certainly does not indicate the looks because the attitudes depend on the human face sections' behaviors. The paper proposes the efficient Facial Emotion Recognition method using MultiSVNNN based on WOA-SCA (the proposed WOA-SCA based Multi-Support Vector Neural Network). Using the ADFES dataset for the disgust expression, our results demonstrate that the proposed work yields average emotion recognition accuracy of 98% associated to 80% and 96% based on BBPSO and MFOPSO, respectively. Investigational consequences indicate the virtuous enactment of the proposed scheme resolutions regarding precision. [ABSTRACT FROM AUTHOR]

In this paper a novel nature-inspired optimization paradigm is proposed called Moth-Flame Optimization (MFO) algorithm. The main inspiration of this optimizer is the navigation method of moths in nature called transverse orientation. Moths fly in night by maintaining a fixed angle with respect to the moon, a very effective mechanism for travelling in a straight line for long distances. However, these fancy insects are trapped in a useless/deadly spiral path around artificial lights. This paper mathematically models this behaviour to perform optimization. The MFO algorithm is compared with other well-known nature-inspired algorithms on 29 benchmark and 7 real engineering problems. The statistical results on the benchmark functions show that this algorithm is able to provide very promising and competitive results. Additionally, the results of the real problems demonstrate the merits of this algorithm in solving challenging problems with constrained and unknown search spaces. The paper also considers the application of the proposed algorithm in the field of marine propeller design to further investigate its effectiveness in practice. Note that the source codes of the MFO algorithm are publicly available at http://www.alimirjalili.com/MFO.html . [ABSTRACT FROM AUTHOR]

Optimization is an important and fundamental challenge to solve optimization problems in different scientific disciplines. In this paper, a new stochastic nature-inspired optimization algorithm called Pelican Optimization Algorithm (POA) is introduced. The main idea in designing the proposed POA is simulation of the natural behavior of pelicans during hunting. In POA, search agents are pelicans that search for food sources. The mathematical model of the POA is presented for use in solving optimization issues. The performance of POA is evaluated on twenty-three objective functions of different unimodal and multimodal types. The optimization results of unimodal functions show the high exploitation ability of POA to approach the optimal solution while the optimization results of multimodal functions indicate the high ability of POA exploration to find the main optimal area of the search space. Moreover, four engineering design issues are employed for estimating the efficacy of the POA in optimizing real-world applications. The findings of POA are compared with eight well-known metaheuristic algorithms to assess its competence in optimization. The simulation results and their analysis show that POA has a better and more competitive performance via striking a proportional balance between exploration and exploitation compared to eight competitor algorithms in providing optimal solutions for optimization problems. [ABSTRACT FROM AUTHOR]