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Structural engineering is a field with several optimization approaches. Concordantly, the goal in optimum design is the generation of the lightest, cheapest, or most sustainable engineering design for any type of structure or structural member. In this study, some optimization approaches were presented for structures including 25 and 72-bar space trusses. To find the smallest weights were ensured for both structures. Optimization approaches related to metaheuristics and modifications of them were developed by inspired by features of nature, abilities of lives and mechanisms of chemical/physical processes. The employed three techniques that are flower pollination algorithm, Jaya algorithm and the hybrid algorithm developed via them were compared to each other through some parameters by calculating different statistical expressions containing the best, average and standard deviation of the objective function. Also, the performance of whole metaheuristic versions is evaluated with some literature researches to find the most effective one to minimize the structural weights. These results have observed that the all versions of proposed metaheuristic algorithms are generally successful, and effective to minimize the objective function comparison to algorithms used in previous studies. Moreover, the best values of objective functions can be decreased at the 0.02–0.06 rates as minimum and maximum compared to the previous studies and algorithms, respectively. [ABSTRACT FROM AUTHOR]

In this study, we proposed a new metaheuristic algorithm called Electrical Search Algorithm (ESA). The proposed algorithm is based on the movement of electricity in high-resistive areas such as wood, glass, and gases. ESA has a unique initialization scheme that only one agent initializes at the lower and upper bounds of the search space, which creates structures called poles. After that, ESA uses unique exploration and exploitation strategies to search. The search mechanism is based on electrons moving to opposite poles. ESA differs from other metaheuristics compared to its initialization scheme, pole search mechanism, and update strategy of the best solutions. ESA was tested with the "100-Digit Challenge" benchmark functions in the IEEE-CEC-2019, four well-known benchmark functions, and an np-hard clustering problem. For the clustering problem, we used four well-known datasets: Iris, Wine, Seeds, and Hepatitis C Virus. ESA was compared with seven different metaheuristic algorithms on these well-known benchmark functions, and the results of the clustering problem were compared with the K-Means algorithm. Additionally, Friedman Signed Rank and post hoc Wilcoxon Test were run to show the significance of the results. In all of the well-known benchmark functions, ESA either offered the best results or similar results to other compared algorithms. The score of the ESA on the IEEE-CEC-2019 benchmark functions shows us that even with the minor evaluation numbers, ESA can achieve similar results to the competing algorithms. Results show that ESA has a robust mechanism for not trapping in local points and moves slow but persistent rate. [ABSTRACT FROM AUTHOR]

How to steadily find satisfactory solutions for high-dimensional multimodal and composition optimization problems is still a challenging issue. To fight against this pain-point problem, we propose sardine optimization algorithm (SOA) with agile locality and globality strategies for real optimization problems. Inspired by the survival philosophy of sardines, SOA simulates the transformation, migration, reproduction, elimination, and spread of sardines. As a varied-population-size optimization algorithm, the features of SOA are summarized as two key points. (i) Agile locality and globality strategies use adjacent and corresponding period ratios to control the local and global search behaviors. To our best knowledge, these strategies are a new technical road to balance exploration and exploitation efforts. (ii) Besides, SOA uses unique search operators based on the center movement of sardine schools. Specifically, when the center of one sardine school moves in these search operators, all sardines in this school also move in the same direction. This mobility style is different from most meta-heuristic algorithms, as far as we know. We used all unconstrained optimization problems in the CEC2013 test suite and six real-world constrained optimization problems as our benchmarks. SOA outperforms eight algorithms (like the two winning algorithms of CEC2013 and CEC2014), especially for high-dimension multimodal and composition optimization problems. For instance, Wilcoxon results between SOA and the two winning algorithms of CEC2013 and CEC2014 for 90-dimensional unconstrained optimization problems of the CEC2013 test suite are 16:10 and 18:8. The coding of SOA can be downloaded from "https://github.com/1654402787/SOA" (unzip password: soasoasoa1357). [ABSTRACT FROM AUTHOR]

Different sports require different set of equipment with different specifications and configurations. The oar-shaft optimal configurations and mechanical characteristics are important problem in rowing sport engineering. The oar-shaft design has a major impact on the effectiveness, performance, and safety of rowing technique. In this paper, the grey wolf optimizer is proposed for tackling the problem of determining the optimal design of the oar-shaft, with continuous design variables, so that all constraints consisting of the stress factor-of-safety (FoS) and target deflection ( Δ t ) are completely satisfied. The efficiency of the proposed GWO algorithm for solving the oar-shaft optimization is demonstrated through sculling and sweeps oar-shaft construction with two type of shafts (Ultra Light and Ultra Light Nano) of different stiffness. For experimental results, we established dataset of 31 shafts' cases using the BRACA-SPORT stiffness options. The experimental result demonstrates that the oar-shaft optimal designs for minimizing the oar-shaft rotational inertia identified with the GWO met the oar-shaft deflection and stress constraints, and the feasible ranges of design parameters—radius, inboard and outboard setting, length and tube wall thickness. The optimal results of 31 shafts construction show that the deflection is an important for minimizing rotational inertia of the shaft and blade about the oarlock. While a lot of studies have focused on various aspects of the shaft construction, this paper initially addressed the problem of oar-shaft optimization in metaheuristics terms for the first time. [ABSTRACT FROM AUTHOR]

Since the past few years, several metaheuristic algorithms, inspired by the natural processes, have been introduced to solve different complex optimization problems. Studying and comparing the convergence, computational burden and statistical significance of those metaheuristics are helpful for future algorithmic development and their applications. This paper focuses on comparing the optimization performance of classical dragonfly algorithm (DA) and its seven different variants, i.e., hybrid memory-based dragonfly algorithm with differential evolution (DADE), quantum-behaved and Gaussian mutational dragonfly algorithm (QGDA), memory-based hybrid dragonfly algorithm (MHDA), chaotic dragonfly algorithm (CDA), biogeography-based Mexican hat wavelet dragonfly algorithm (BMDA), hybrid Nelder–Mead algorithm and dragonfly algorithm (INMDA) and hybridization of dragonfly algorithm and artificial bee colony (HDA) while solving 80 CEC-2021 benchmark problems. It is observed that the convergence rates of different variants of DA algorithm vary, and the corresponding computational times for such variations are also evaluated. This paper finally ranks DA and its variants according to their convergence efficiency and Friedman test. The DADE, QGDA, BMDA and DA evolve out as the most efficient algorithms for solving the considered CEC-2021 benchmark problems. [ABSTRACT FROM AUTHOR]

This study fixates on determining the optimum laser input parameters that simultaneously meet the desired kerf width and depth during CO2 laser cutting of various polymethylmethacrylate (PMMA) sheets. It has three contributions. The first is to model the cutting process of PMMA by polynomial curve fitting as a function of laser power, laser speed, and standoff distance. R-squared (R2), adjusted R2 and root-mean-square error (RMSE) criteria were taken into account to measure the performance of the proposed model. The effect of laser parameters on the process is investigated by analysis of variance (ANOVA) and sensitivity analysis. The second is to optimize the derived nonlinear regression models using genetic algorithm (GA), particle swarm optimization (PSO), whale optimization algorithm (WOA) and ant lion optimization (ALO) metaheuristic methods and compare the performances of the algorithms. The third is to compare the adequacy of the optimization process with the artificial neural network (ANN). The investigations exhibited that the best-fitting polynomials are obtained with the R2 and adjusted R2 values of over 99% and 97%, respectively. ANOVA and sensitivity test revealed that the sensitivity of the laser power, which is the most effective parameter, was 150 at low powers and decreased to 0 as the power value increased. When the nozzle distance is 4.1, the proposed metaheuristics gave effective and sufficiently accurate results. PSO stood out in terms of both best cost value (3.49 × 10–8) and relative error value (0.19%). The relative error of the ANN was found as 3% in terms of kerf depth. [ABSTRACT FROM AUTHOR]

The marine predator algorithm is a new nature-inspired metaheuristic algorithm that mimics biological interaction between marine predators and prey. It has been also stated from the literature that this algorithm can solve many real-world optimization problems which made it a new popular optimization technique for the researchers. However, there is still a deficiency in the marine predator algorithm such as the inability to produce a diverse initial population with high productivity, lack of quick escaping of the local optimization, and lack of widely and broadly exploration of the search space. In the present study, a developed version of this algorithm is proposed based on the opposition-based learning method, chaos map, self-adaptive of population, and switching between exploration and exploitation phases. The simulations are performed using MATLAB environment on standard test functions including CEC-06 2019 tests and a real-world optimization problem based on PID control applied to a DC motor to evaluate the performance of the suggested algorithm. The simulation results are compared with the original marine predator algorithm and five state-of-the-art optimization algorithms namely Particle Swarm Optimization, Grasshopper Optimization Algorithm, JAYA Algorithm, Equilibrium optimizer Algorithm, Whale Optimization Algorithm, Differential Search Algorithm, and League Championship Algorithm. Eventually, the simulation results proved that the suggested algorithm has better results compared with other algorithms for the studied case studies. [ABSTRACT FROM AUTHOR]

Feature transformation methods can be used effectively to improve the performance of the machine learning techniques, such as classification and clustering, via enhancing the discrimination among the samples. Additionally, binary representation of features may be necessary in several circumstances. For example, distributed systems may have limited bandwidth, storage, and energy that necessitate rough quantization of the measurements. This paper proposes a random projection-based feature transformation method that maps the data points from the original space into a new binary space. In the proposed transformation method, the random projection process is formulated as an optimization problem. A modified chaotic version of the sine–cosine algorithm is used to find the optimal random projector. The performance of the proposed transformation method is validated using 15 standard UCI benchmark datasets against the linear discernment analysis. Moreover, it is compared to other metaheuristic-based feature reduction methods including the gray wolf optimizer, the genetic algorithm, and the antlion optimizer. The obtained results assure the superiority of the proposed transformation method in terms of classification accuracy and storage requirements. [ABSTRACT FROM AUTHOR]

In this paper, a new metaheuristic algorithm called JAYA algorithm has been adapted for feature selection. Feature selection is a typical problem in machine learning and data mining domain concerned with determining the subset of high discriminative features from the irrelevant, noisy, redundant, and high-dimensional features. JAYA algorithm is initially proposed for continuous optimization. Due to the binary nature of the feature selection problem, the JAYA algorithm is adjusted using sinusoidal (i.e., S-shape) transfer function. Furthermore, the mutation operator controlled by adaptive mutation rate ( R m ) parameter is also utilized to control the diversity during the search. The proposed binary JAYA algorithm with adaptive mutation is called BJAM algorithm. The performance of BJAM algorithm is tested using 22 real-world benchmark datasets, which vary in terms of the number of features and the number of instances. Four measures are used for performance analysis: classification accuracy, number of features, fitness values, and computational times. Initially, a comparison between binary JAYA (BJA) algorithm and the proposed BJAM algorithm is conducted to show the effect of the mutation operator in the convergence behavior. After that, the results produced by the BJAM algorithm are compared against those yielded by ten state-of-the-art methods. Surprisingly, the proposed BJAM algorithm is able to excel other comparative methods in 7 out of 22 datasets in terms of classification accuracy. This can lead to the conclusion that the proposed BJAM algorithm is an efficient algorithm for the problems belonging to the feature selection domain and is pregnant with fruitful results. [ABSTRACT FROM AUTHOR]

Papaya seed waste can be a reliable feedstock for producing valuable bioproducts (biodiesel, biolubricants, beauty products, etc.) due to its high oil content. This article focuses to explore the effects of Soxhlet extraction process conditions (extraction time and seed particle size) on the percent oil yield obtained from papaya seeds. Initially, two mathematical models were developed using response surface methodology (RSM) via central composite design and regression analysis (generalized linear model, GLM) to predict the oil yield. The prediction performance of RSM model was found to be superior than GLM. The extracted oil was characterized by gas chromatography–mass spectrometry (GC–MS) analysis. The analysis of variance results indicated that both factors were strongly significant. Later, crow search algorithm (nature-motivated metaheuristic algorithm) articulated with RSM was utilized for global optimal solution. The maximum yield of 29.96% was obtained at extraction time of 6.5 h and seed particle size of 0.85 mm. The similar results were obtained by desirability function-based optimization approach. The predicted optimal set was also validated further by experimental yield of 31.1% with the variation of < 5%. [ABSTRACT FROM AUTHOR]

Due to the lack of experience and prior knowledge, the selection of the most informative features has become one of the challenging problems in many applications. Recently, many metaheuristic algorithms have widely used to solve the feature selection problem for classification tasks. In this paper, the chaotic atom search optimization (CASO) that integrates the chaotic maps into atom search optimization (ASO) is applied for wrapper feature selection. Twelve different chaotic maps are used to adjust the parameter of CASO through the optimization process, which is beneficial for enhancing the convergence rate and improving the efficiency of ASO algorithm. In this study, twenty benchmark datasets acquired from the UCI machine learning repository are used to validate the performance of CASO in feature selection. Several state-of-the-art metaheuristic algorithms are adopted to examine the efficacy and effectiveness of the proposed approach. Our results indicated that the Logistic-Tent map was the most suitable chaotic map to boost the performance of CASO. The experimental result shows the capability of CASO not only in finding the optimal solution but also in significantly improving the prediction accuracy and reducing the number of features. [ABSTRACT FROM AUTHOR]

Nature-inspired metaheuristic algorithms have significantly influenced the optimization problems, and there has been a great success in finding optimum solutions. The main aim of this paper is to introduce one such technique, runner-root algorithm (RRA), to the antenna community. RRA is based on the concept of plant intelligence to solve an optimization problem resembling a scenario where a plant finds the best location for its survival. The main advantage of RRA over other algorithms is the execution of exploration (global search) and exploitation (local search) phases independently with a built-in mechanism of reinitialization to overcome trapping in local minima. Hence, attempt has been made for the first time to apply this algorithm to optimize excitation current amplitudes and positions of antenna elements for linear array configurations with the objective of suppressing the sidelobe levels and/or placing nulls at prescribed locations referring to the radiation patterns. Different case studies have illustrated that RRA is capable of performing linear antenna array optimization with promising results in comparison with other well-established metaheuristic algorithms. And hence such demonstration has identified RRA as a potential candidate in the optimization domain. [ABSTRACT FROM AUTHOR]

The optimization of continuous space poses a great challenge among the scientific community. When the objective function is nonlinear, the choices of direct search spaces are preferred over the other methods. The use of the hybrid algorithm for these types of optimization is becoming increasingly popular. This study introduced a self-adaptation procedure in a single objective hybrid algorithm and its application for unconstrained and constrained optimization test functions. This single objective hybrid algorithm is based on two popular metaheuristic algorithms, namely the cuckoo search and covariance matrix adaptation evolution strategy. Self-adaptation is a popular way of parameter selection and has a significant place in the computing field. The adaptation is introduced in two significant parameters of this algorithm. Five metaheuristic algorithms, namely cuckoo search, covariance matrix adaptation evolution strategy, particle swarm intelligence, firefly algorithm, and the newly introduced self-adapted single objective hybrid algorithm, were analyzed using unconstrained (unimodal and multimodal) and constrained benchmark test functions. An encouraging performance of this proposed algorithm for unconstrained and constrained test functions was observed. [ABSTRACT FROM AUTHOR]

Classical optimization algorithms are insufficient in large-scale combinatorial problems and in nonlinear problems. Hence, heuristic optimization algorithms have been proposed. General purposed metaheuristic methods are evaluated in nine different groups: biology-based, physics-based, social-based, music-based, chemical-based, sports-based, mathematics-based, and hybrid methods which are combinations of these. Recently, a sports-based search and optimization algorithm entitled as league championship algorithm (LCA) has been proposed. LCA is a population-based, metaheuristic optimization algorithm that simulates a championship for general optimization with artificial teams and artificial league for several weeks. In this algorithm, according to the league program, a number is given to the couple of teams that will match and the result of match is determined as loser or winner. Winning or losing the game is closely related to power of teams. Teams are intended to improve the formation of the current team throughout the season to win the game in the coming weeks. Chaotic maps seem to improve the convergence speed and accuracy of optimization algorithms. Increasing global convergence speed and prevention of getting stuck on local solutions of LCA with chaos have been proposed for the first time in this study. In this paper, six different chaotic LCAs have been proposed and explained in detail. Comparative performance results have been examined in complex benchmark functions. Promising results have been obtained from the experimental results. Combining results appeared in different fields like LCA and complex dynamics can increase quality in some optimization problems and the chaos can be the wanted process. [ABSTRACT FROM AUTHOR]