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• This paper proposes an enhanced SCA algorithm variant, called ARSCA. • ARSCA improves the convergence accuracy and avoids premature convergence. • This paper compares ARSCA with classical algorithms, variations of other algorithms. • ARSCA is applied to six engineering design problems and its results are satisfactory. The sine cosine algorithm (SCA) is a well-known meta-heuristic optimization algorithm. SCA has received much attention in various optimization fields due to its simple structure and excellent optimization capabilities. However, the dimension of objective function also increases with the increasing complexity of optimization tasks. This makes the original SCA appear to have insufficient optimization capability and likely to fall into premature convergence. A multi-mechanism acting variant of SCA, called ARSCA, is proposed to address the above deficiencies. ARSCA is an enhanced SCA algorithm based on the adaptive quadratic interpolation mechanism (AQIM) and Rounding mechanism (RM). RM enables a more balanced state between exploration and exploitation of the ARSCA. AQIM enhances local exploitation capabilities. To verify the performance of ARSCA, we compared ARSCA with some advanced traditional optimization algorithms and variants of algorithms for 30 consecutive benchmark functions of IEEE CEC2014. In addition, ARSCA was applied to 6 constrained engineering optimization problems. These six algorithms include the tension–compression spring design problem, the welded beam design problem, the pressure vessel design problem, the I-beam design problem, the speed reducer design problem, and the three-bar design problem. Experimental results show that ARSCA outperforms its competitors in both the solution quality and the ability to jump out of the local optimum. The relevant codes for the paper are publicly available at https://github.com/YangXiao9799/paper_ARSCA. [ABSTRACT FROM AUTHOR]

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]

This paper mainly focuses on the optimal design of a grid-dependent and off-grid hybrid renewable energy system (RES). This system consists of Photovoltaic (PV), Wind Turbine (WT) as well as Fuel Cell (FC) with hydrogen gas tank for storing the energy in the chemical form. The optimal components sizes of the proposed hybrid generating system are achieved using a novel metaheuristic optimization technique. This optimization technique, called Improved Artificial Ecosystem Optimization (IAEO), is proposed for enhancing the performance of the conventional Artificial Ecosystem Optimization (AEO) algorithm. The IAEO improves the convergence trends of the original AEO, gives the best minimum objective function, reaches the optimal solution after a few iterations numbers as well as reduces the falling into the local optima. The proposed IAEO algorithm for solving the multiobjective optimization problem of minimizing the Cost of Energy (COE), the reliability index presented by the Loss of Power Supply Probability (LPSP), and excess energy under the constraints are considered. The hybrid system is suggested to be located in Ataka region, Suez Gulf (latitude 30.0, longitude 32.5), Egypt, and the whole lifetime of the suggested case study is 25 years. To ensure the accurateness, stability, and robustness of the proposed optimization algorithm, it is examined on six different configurations, representing on-grid and off-grid hybrid RES. For all the studied cases the proposed IAEO algorithm outperforms the original AEO and generates the minimum value of the fitness function in less execution time. Furthermore, comprehensive statistical measurements are demonstrated to prove the effectiveness of the proposed algorithm. Also, the results obtained by the conventional AEO and IAEO are compared with those obtained by several well-known optimization algorithms, Particle Swarm Optimization (PSO), Salp Swarm Algorithm (SSA), and Grey Wolf Optimizer (GWO). Based on the obtained simulation results, the proposed IAEO has the best performance among other algorithms and it has successfully positioned itself as a competitor to novel algorithms for tackling the most complicated engineering problems. [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]

In demand side management (DSM), scheduling of appliances based on consumer-defined priorities is an important task performed by a home energy management controller (HEMC). This paper presents a DSM technique that is capable of controlling loads within a smart home considering time-varying appliances’ priorities. An evolutionary accretive comfort algorithm (EACA) is developed based on four postulations that allow time-varying priorities to be quantified in time and device-based features. Based on the input data considering the appliances’ power ratings, its time of use, and absolute comfort derived from priorities, the EACA is able to generate an optimal energy consumption pattern which would give maximum satisfaction at a predetermined user budget. A cost per unit comfort index ( χ ) which relates the consumer expenditure to the achievable comfort is also demonstrated. To test the applicability of the proposed EACA, three budget scenarios of $1.5/day, $2.0/day and $2.5/day are performed. The results of each of the scenarios using EACA are compared to five base cases in which the appliances are randomly used. The simulation results revealed that the proposed EACA obtained an optimal absolute comfort with reduced cost per unit comfort values for all three scenarios within the budget limits. [ABSTRACT FROM AUTHOR]

Sine Cosine Algorithm (SCA) that is one of the population-based metaheuristic optimization algorithms basically consists of the updating mechanism based on sine and cosine functions. In this algorithm, a few random and adaptive variables are also utilized for more effective motions of the candidate solutions. SCA has some drawbacks like other some metaheuristic algorithms. SCA tends to be stuck into the local regions in the search space and this affects negatively on the computational effort required to find the best solution point in the search space. This paper presents four different improved versions of SCA. The proposed improvements on original SCA are the innovations on the updating mechanism of SCA. To evaluate the performances of Improved Sine Cosine Algorithms (ImpSCAs), well-known numerical optimization problems including CEC 2014 test suite are used. Firstly, different analyses of the proposed ImpSCAs are dealt with such as the convergence analysis, search history analysis, trajectory analysis, average distance analysis, and computational complexity analysis. Secondly, the proposed four versions of ImpSCAs are compared with the original SCA for CEC 2014 benchmark problems with dimension sizes of 10D, 30D and 50D. Finally, original SCA and ImpSCAs are adapted to select optimal feature combination and they are tested for 10 feature selection datasets taken from the UCI machine learning repository. The benchmark results show that the performances of the ImpSCA 1 , ImpSCA 2 , and ImpSCA 4 are better than that of the original SCA. From the feature selection results, it is observed that three versions of ImpSCAs (except ImpSCA 3) outperform the original SCA in 80% of the datasets. Source codes of ImpSCAs are publicly available at https://github.com/uguryuzgec/ImpSCAs. • We focused to improve the exploration and exploitation ability of the original SCA. • The basic idea is based on the innovations on the update mechanism. • The ISCAs were evaluated using CEC2014 benchmark test suite. • The proposed ISCAs were tested for 10 popular UCI datasets. • Three versions of ISCAs (except ISCA 3) outperform the SCA in 80% of the datasets. [ABSTRACT FROM AUTHOR]

It is always desirable to keep the number of components in compositional simulation low due to the high CPU time and storage space requirement; on the other hand, the model's accuracy in describing the phase behavior decreases with reducing the number of components. As a result, an optimal lumping approach is needed to group the components to attain high accuracy in the least amount of time. This paper's main objective is to find the best lumping scheme to maintain the split model's accuracy and reach an optimal lumped model in the least amount of time. In accordance with this purpose, five different evolutionary-based metaheuristic optimization algorithms known as genetic algorithm (GA), differential evolution (DE), harmony search (HS), imperialist competitive algorithm (ICA), and shuffled-frog leaping algorithm (SFLA) are incorporated into the objective function developed to cluster heavy fractions using the k-means algorithm. To find the optimal algorithm, the best cost of the objective function and the number of function evaluations (NFE) have been compared, and the final results were substantiated using flash calculation outputs, such as density, viscosity, phase diagram, and Wilson plot. The results indicate that the proposed algorithms are capable of finding the optimal lumped model with high accuracy for all reservoir samples tested in this study. This study also marked ICA as the best evolutionary-based optimization algorithm that can be used in clustering problems. • Clustering approach using Evolutionary-based algorithms to lump heavy-end component. • The order of algorithms efficiency to lump is ICA > SFLA > GA ≈ DE ≫ HS. • ICA and SFLA attain the best cost in the lowest number of function evaluation. • The efficiency of ICA does not reduce with the increase in the number of components. • 16 components are adequate enough to present ICA-16 as the optimum lumping strategy. [ABSTRACT FROM AUTHOR]

• A new optimization algorithm based on Lichtenberg figure pattern is presented. • Difussion Limited Aggregation is formulated to create new agents in search space. • Lichtenberg figure pattern presents exploration–exploitation balance. • Tested on complex functions and problems and compared with renowned metaheuristics. This paper proposes a novel global optimization algorithm called Lichtenberg Algorithm (LA), inspired by the Lichtenberg figures patterns. Optimization is an essential tool to minimize or maximize functions, obtaining optimal results on costs, mass, energy, gains, among others. Actual problems may be multimodal, nonlinear, and discontinuous and may not be minimized by classical analytical methods that depend on the gradient. In this context there are metaheuristics algorithms inspired by natural phenomena to optimize real problems. There is no algorithm that is the worst or the best, but more efficient for a given type of problem. Thus, an unprecedented metaheuristic algorithm was created inspired by the physical phenomenon of radial intra-cloud lightning and Lichtenberg figures, successfully exploiting the fractal power and it is different from many in the literature as it is a hybrid algorithm composed of methods of search based on population and trajectory. Several test functions, including a design problem in a welded beam, were used to verify the robustness and to validate the Lichtenberg Algorithm. In all cases, the results were satisfactory when compared to those in the literature. LA shown to be a powerful optimization tool for both unconstraint optimizations and real problems with linear and nonlinear constraints. [ABSTRACT FROM AUTHOR]