Metaheuristic Algorithm For Global Optimization Research Articles

Yellow Ground Squirrel Algorithm (YGSA): A Novel Metaheuristic Algorithm for Global Optimization

In recent years, numerous methods have emerged for optimizing problems inspired by nature. Each of these methods, according to their nature, has applications for solving specific problems with unique characteristics. Given the wide array of optimization problems across various fields, introducing novel algorithms with distinct behaviors can be both appealing and practical. In this article, a nature-inspired meta-heuristic optimization algorithm called Yellow Ground Squirrel Algorithm (YGSA) is introduced. The main idea behind YGSA is to simulate the natural behavior of a yellow ground squirrel in terms of pursuit and evasion from a farmer (the predator) and reaching the nest. The objective of the proposed algorithm is to enhance and attain a more equitable equilibrium between the fundamental stages of exploration and exploitation in contrast to preceding approaches. This balance arises from the fact that the yellow ground squirrel, during its escape, simultaneously tries to increase the distance from the farmer (the predator) and decrease the distance to its nest (burrow). To validate the proposed algorithm, 56 benchmark evaluation functions from various unimodal and multimodal types have been evaluated. The evaluation results are compared with the HBO, EBO-CMAR, FO, SCA, PSO, CS, GSA, MVO, and WSO algorithms. The optimization results for unimodal test functions demonstrate the high exploitation capability of YGSA in approaching the optimal solution, while the optimization results for multimodal test functions indicate the strong exploration ability of YGSA in finding the main optimal region in the search space.

Nizar optimization algorithm: a novel metaheuristic algorithm for global optimization and engineering applications

Nizar optimization algorithm: a novel metaheuristic algorithm for global optimization and engineering applications

Swarm Intelligence-Homotopy Hybrid Optimization-Based ANN Model for Tunable Bandpass Filter

High-performance tunable radio frequency (RF)/ microwave and millimeter-wave filter design is a challenging task due to the lack of a basic theory. The filtering characteristics are highly sensitive to the variation of tuning elements that are commonly modeled and achieved by optimization algorithms. However, those optimizations only provide satisfactory results with a good set of initial parameters. Such range-limited optimization algorithms generally have issues of falling into local optima, slow convergence, and cumbersome implementation. To mitigate this problem, for the first time, a topology-based local optimizer is integrated with metaheuristic global optimization algorithms in this work. We have hybridized the homotopy method with an improved whale optimization algorithm (WOA) and a gray wolf optimization (GWO) algorithm. In this work, an artificial neural network (ANN) is formulated and studied, which has twofold applications. First, ANN is used as a surrogate model to represent the time-consuming electromagnetic (EM) model in expediting the hybrid optimization process of tunable filters. Second, an ANN model is developed on data generated by the proposed optimization algorithm for predicting tunable circuit parameters at different tuning stages. The proposed ANN model-based algorithm is then applied to a fifth-order lumped-element tunable circuit and two fourth-order full-wave EM simulation models of two tunable bandpass filters (tBPFs). The calculated results out of the ANN model demonstrate a good agreement with simulation and measurement counterparts.

Levy flight incorporated hybrid learning model for gravitational search algorithm

Gravitational search algorithm (GSA) is a widely used meta-heuristic algorithm for global optimization. Its strong social interaction abilities and easy to implement nature make it more applicable than its contemporaries. However, multi-modality always remains a challenging task for GSA search mechanism due to its incapabilities towards premature convergence. This paper proposes a novel GSA variant called ‘Levy flight incorporated gravitational search algorithm with an adaptive spiral strategy (LevyGSA)’ to address the shortcomings of GSA with the following developments: First, a levy flight associated position update strategy for elite agents of the swarm is proposed for a better interior search. Secondly, an adaptive spiral update strategy is introduced for the rest swarm to balance the trade-off between exploration and exploitation for a robust search. Finally, a dimensional reduction based strategy for enhancing the local search around the known global optimal region is introduced. The proposed algorithm is tested over 23 classical test problems and 30 CEC 2014 test problems. The numerical results demonstrate the outstanding performance of the proposed algorithm through which it outperforms the well-known existing meta-heuristics along with recent GSA variants. Furthermore, finding more accurate solutions for five engineering design problems validates its applicability in real-world scenarios.

Nutcracker optimizer: A novel nature-inspired metaheuristic algorithm for global optimization and engineering design problems

This work presents a novel nature-inspired metaheuristic called Nutcracker Optimization Algorithm (NOA) inspired by Clark’s nutcrackers. The nutcrackers exhibit two distinct behaviors that occur at separate periods. The first behavior, which occurs during the summer and fall seasons, represents the nutcracker’s search for seeds and subsequent storage in an appropriate cache. During the winter and spring seasons, another behavior based on the spatial memory strategy is regarded to search for the hidden caches marked at different angles using various objects or markers as reference points. If the nutcrackers cannot find the stored seeds, they will randomly explore the search space to find their food. NOA is herein proposed to mimic these various behaviors to present a new, robust metaheuristic algorithm with different local and global search operators, allowing it to solve various optimization problems with better outcomes. NOA is evaluated on twenty-three standard test functions, test suites of CEC-2014, CEC-2017, and CEC-2020 and five real-world engineering design problems. NOA is compared with three classes of existing optimization algorithms: (1) SMA, GBO, EO, RUN, AVOA, RFO, and GTO as recently-published algorithms, (2) SSA, WOA, and GWO as highly-cited algorithms, and (3) AL-SHADE, L-SHADE, LSHADE-cnEpSin, and LSHADE-SPACMA as highly-performing optimizers and winners of CEC competition. NOA was ranked first among all methods and demonstrated superior results when compared to LSHADE-cnEpSin and LSHADE-SPACMA as the best-performing optimizers and the winners of CEC-2017, and AL-SHADE and L-SHADE as the winners of CEC-2014.

Fuzzy adaptive jellyfish search-optimized stacking machine learning for engineering planning and design

This paper presents a novel fuzzy adaptive jellyfish search-optimized stacking system (FAJS-SS) that integrates the jellyfish search (JS) optimizer, the fuzzy adaptive (FA) logic controller, and stacking ensemble machine learning. First, FA logic is incorporated into JS optimizer to construct an efficient metaheuristic algorithm for global optimization. The proposed algorithm is benchmarked against various well-known optimizers using mathematical functions. The FAJS optimizer is then used to optimize the hyperparameters of the stacking system (SS). Cases that involve construction productivity, the compressive strength of a masonry structure, the shear capacity of reinforced deep beams, the axial strength of steel tube-confined concrete, and the resilient modulus of subgrade soils were investigated. Results of analyses reveal that the FAJS-SS predicts more accurately than the other machine learning systems in the literature. Accordingly, the proposed fuzzy adaptive metaheuristic-optimized stacking system is effective for providing engineering informatics in the planning and design phase.

GOZDE: A novel metaheuristic algorithm for global optimization

This study proposes a new metaheuristic algorithm, called “Geometric Octal Zones Distance Estimation” (GOZDE) algorithm to solve global optimization problems. The presented GOZDE employs a search scheme with the information sharing between the zones considering the distance of the zones utilizing median values. The whole population represents the eight zones that are the combination of different search strategies to guide knowledge dissemination from one zone to others in the search space. To demonstrate the effectiveness of the proposed optimizer, it is compared with two classes of metaheuristics, which are (1) GA, PSO, DE, CS and HS as the classical metaheuristics and (2) BWO, SSA, MVO, HHO, ChOA, AOA and EBOwithCMAR as the up-to-date metaheuristics. The search capability of the proposed algorithm is tested on two different numerical benchmark sets including low and high dimensional problems. The developed algorithm is also adapted to ten real-world applications to handle constraint optimization problems. In addition, to further analyse the results of the proposed algorithm, three well-known statistical metrics, Friedman, Wilcoxon rank-sum and Whisker-Box statistical tests are conducted. The experimental results statistically show that GOZDE is significantly better than, or at least comparable to the twelve metaheuristic algorithms with outstanding performance in solving numerical functions and real-world optimization problems.

Giant Trevally Optimizer (GTO): A Novel Metaheuristic Algorithm for Global Optimization and Challenging Engineering Problems

Metaheuristic algorithms are becoming powerful methods for solving continuous global optimization and engineering problems due to their flexible implementation on the given problem. Most of these algorithms draw their inspiration from the collective intelligence and hunting behavior of animals in nature. This paper proposes a novel metaheuristic algorithm called the Giant Trevally Optimizer (GTO). In nature, giant trevally feeds on many animals, including fish, cephalopods, and seabirds (sooty terns). In this work, the unique strategies of giant trevally when hunting seabirds are mathematically modeled and are divided into three main steps. In the first step, the foraging movement patterns of giant trevallies are simulated. In the second step, the giant trevallies choose the appropriate area in terms of food where they can hunt for prey. In the last step, the trevally starts to chase the seabird (prey). When the prey is close enough to the trevally, the trevally jumps out of the water and attacks the prey in the air or even snatches the prey from the water surface. The performance of GTO is compared against state-of-the-art metaheuristics for global optimization on a set of forty benchmark functions with different characteristics and five complex engineering problems. The comparative study, scalability analysis, statistical analysis based on the Wilcoxon rank sum test, and the findings suggest that the proposed GTO is an efficient optimizer for global optimization. Note that the MATLAB source codes for GTO will be publicly available after the acceptance of the paper.

War Strategy Optimization Algorithm: A New Effective Metaheuristic Algorithm for Global Optimization

This paper proposes a new metaheuristic optimization algorithm based on ancient war strategy. The proposed War Strategy Optimization (WSO) is based on the strategic movement of army troops during the war. War strategy is modeled as an optimization process wherein each soldier dynamically moves towards the optimum value. The proposed algorithm models two popular war strategies, attack and defense strategies. The positions of soldiers on the battlefield are updated in accordance with the strategy implemented. To improve the algorithm’s convergence and robustness, a novel weight updating mechanism and a weak soldier’s relocation strategy are introduced. The proposed war strategy algorithm achieves good balance of the exploration and exploitation stages. A detailed mathematical model of the algorithm is presented. The efficacy of the proposed algorithm is tested on 50 benchmark functions and four engineering problems. The performance of the algorithm is compared with ten popular metaheuristic algorithms. The experimental results for various optimization problems prove the superiority of the proposed algorithm.

Tuna Swarm Optimization: A Novel Swarm-Based Metaheuristic Algorithm for Global Optimization.

In this paper, a novel swarm-based metaheuristic algorithm is proposed, which is called tuna swarm optimization (TSO). The main inspiration for TSO is based on the cooperative foraging behavior of tuna swarm. The work mimics two foraging behaviors of tuna swarm, including spiral foraging and parabolic foraging, for developing an effective metaheuristic algorithm. The performance of TSO is evaluated by comparison with other metaheuristics on a set of benchmark functions and several real engineering problems. Sensitivity, scalability, robustness, and convergence analyses were used and combined with the Wilcoxon rank-sum test and Friedman test. The simulation results show that TSO performs better compared to other comparative algorithms.

The Archerfish Hunting Optimizer: A Novel Metaheuristic Algorithm for Global Optimization

Global optimization solves real-world problems numerically or analytically by minimizing their objective functions. Most of the analytical algorithms are greedy and computationally intractable (Gonzalez in Handbook of approximation algorithms and metaheuristics: contemporary and emerging applications, vol. 2. CRC Press, Boca Raton, 2018). Metaheuristics are generally nature-inspired optimization algorithms. They numerically find a near-optimal solution for optimization problems in a reasonable amount of time. We propose a novel metaheuristic algorithm for global optimization. It is based on the shooting and jumping behaviors of the archerfish for hunting aerial insects. We name our proposed algorithm the archerfish hunting optimizer (AHO). The AHO algorithm has two parameters (the swapping angle and the attractiveness rate) to set. We execute the AHO algorithm using five different values for each parameter. In all, we perform 25 simulations for four distinct values of the search space dimension (i.e., 5, 10, 15, and 20). We run the Friedman test to determine the best values of parameters for each dimension. We perform three different comparisons to validate the proposed algorithm’s performance. First, AHO is compared to 12 recent metaheuristic algorithms (the accepted algorithms for the 2020’s competition on single-objective bound-constrained numerical optimization) on ten test functions of the benchmark CEC 2020 for unconstrained optimization. The experimental results are evaluated using the Wilcoxon signed-rank test. Experimental outcomes show that the AHO algorithm, in terms of robustness, convergence, and quality of the obtained solution, is significantly competitive compared to state-of-the-art methods. Second, the performance of AHO and three recent metaheuristic algorithms is evaluated using five engineering design problems taken from the benchmark CEC 2020 for non-convex constrained optimization. The obtained results are ranked using the ranking scheme detailed in the corresponding paper, and the obtained ranks illustrate that AHO is very competitive when opposed to the considered algorithms. Finally, the performance of AHO in solving five engineering design problems is assessed and compared to several well-established state-of-the-art algorithms. We analyzed the obtained numerical results in detail. These results show that the AHO algorithm is significantly better than, or at least comparable to the considered algorithms with very efficient performance in solving many optimization problems. The statistical indicators illustrate that the AHO algorithm has a high ability to significantly outperform the well-established optimizers.

Computational Intelligence for Modeling, Control, Optimization, Forecasting and Diagnostics in Photovoltaic Applications [Book News

This book describes describes applications of machine learning techniques to maximum power point tracking (MPPT), PV-cell design and sizing, and hybridmodule design and modeling. PV-cell parameter extraction and estimation are proposed using moth–flame and chaotic optimization or deterministic and metaheuristic global optimization algorithms. Neuro-fuzzy inference systems (ANFIS) and a particle swarm optimization-artificial neural network model are applied to PV power output forecasting, while a domain adaptation of deep neural networks is proposed for multistep solar irradiance forecasting. Machine learning approaches are also presented for PV power prediction based on available environmental parameters. Additionally, PV plant operation failure modes and module deterioration diagnosis are studied using complex network and image analysis. After a preface, the book includes 16 carefully selected articles to cover recent trends in machine learning applications to PV systems. The book is an open access tool for engineers and researchers in applying machine learning methods to PV systems. The book is also suited for students willing to further use machine learning skills on PV applications and is a valuable resource for practicing professionals in need of understanding and pursuing advanced trends in PV systems.

Optimization configuration of gas path sensors using a hybrid method based on tabu search artificial bee colony and improved genetic algorithm in turbofan engine

The gas turbine engine is usually working in extreme engine conditions, especially the components along the gas path are bearing the harsh environments, such as high temperature, high pressure, strong vibration and so on, which are prone to failures. The need for security and safety makes it necessary to develop an accurate and efficient monitoring and diagnostic scheme for the gas turbine components. Sensors along the gas path provide the health status information that plays an important role in the monitoring and diagnostic scheme. Owing to the harsh installation environments, the gas path sensors are so limited that it is critical to choose the optimal sensors combination to provide rich and effective status information for health monitoring and fault diagnosis. In this study, the sensitivity of gas path parameters under various gas path component faults is firstly analyzed in a turbofan engine, which verifies that diagnosis results depend sensitively on the types and orders of sensors chosen along gas path. Therefore, an optimization scheme of gas path sensors for fault diagnosis is designed and implemented using three different meta-heuristic global optimization algorithms. The genetic algorithm (GA) is firstly exploited and improved to adapt to the real problem of sensors optimization, which has been shown to be with strong convergence ability but low computation efficiency. The artificial bee colony algorithm combining tabu search (TSABC) has the characteristics of easily realization, less parameters tuning and fast searching speed, which will be seen as a more effective meta-heuristic and proved to achieve good performance of sensors optimization and fault diagnosis, however, this algorithm still exists the defect of slow convergence. Therefore, a hybrid algorithm based on TSABC and IGA (TSABC-IGA) has been then presented and compared, in which the TSABC with fast searching speed is used to obtain the initial optimal population and the IGA with strong convergence ability is exploited to choose the ultimate optimal sensors combination. The experimental studies show that the hybrid algorithm is capable of producing better or at least promising results compared to the mentioned optimization techniques for all of the fault scenarios.

The Solar System Algorithm: A Novel Metaheuristic Method for Global Optimization

A novel metaheuristic algorithm for global optimization, called the Solar System Algorithm (SSA), is presented. The proposed algorithm imitates the orbiting behavior of some objects found in the solar system: i.e., Sun, planets, moons, stars, and black holes. SSA is used to solve five well-known engineering design problems: three-bar truss, pressure vessel, tension/compression spring, welded beam, and gear train. The obtained results are compared to 16 state-of-the-art metaheuristic algorithms. They show that SSA is very competitive in solving the considered engineering problems. In addition, the performance of SSA is evaluated on the benchmarks CEC 2014 and CEC 2020. The experimental results are compared to 27 (12 for CEC 2014 and 15 for CEC 2020) metaheuristic algorithms. They demonstrate that SSA is very promising in finding efficient solutions.

Projectiles Optimization: A Novel Metaheuristic Algorithm for Global Optimization

Metaheuristic optimization algorithms are a relatively new class of optimization algorithms that are widely used for difficult optimization problems in which classic methods cannot be applied and are considered as known and very broad methods for crucial optimization problems. In this study, a new metaheuristic optimization algorithm is presented, the main idea of which is inspired by models in kinematics. This algorithm obtains better results compared to other optimization algorithms in this field and is able to explore new paths in its search for desirable points. Hence, after introducing the projectiles optimization (PRO) algorithm, in the first experiment, it is evaluated by the determined test functions of the IEEE congress on evolutionary computation (CEC) and compared with the known and powerful algorithms of this field. In the second try out, the performance of the PRO algorithm is measured in two practical applications, one for the training of the multi-layer perceptron (MLP) neural networks and the other for pattern recognition by Gaussian mixture modeling (GMM). The results of these comparisons are presented in various tables and figures. Based on the presented results, the accuracy and performance of the PRO algorithm are much higher than other existing methods.

A new calibration of the Heston Stochastic Local Volatility Model and its parallel implementation on GPUs

In this article we propose a new more general calibration of the Heston Stochastic-Local Volatility (HSLV) model. More precisely, the main contribution is to perform the direct calibration of the whole set of parameters at the same time instead of the usual two steps procedure. Moreover, the proposed approach allows to use exotic options to calibrate the HSLV model, thus making it more flexible and general. However, as there are no analytical formulas available to price exotic options to calibrate the model, the cost function (the HSLV pricer) involved in the calibration process must be computed using Monte Carlo methods, thus leading to a highly demanding computational problem. Therefore, we also propose efficient parallel GPU implementations of Monte Carlo techniques for the pricers. Furthermore, for solving the resulting global optimization problem, we develop customized parallel multi-CPU implementations of two of the most common stochastic metaheuristic global optimization algorithms: Differential Evolution and Simulated Annealing. A comparison between both algorithms has been made. This second level of parallelization has been carried out by the implementation of the cost function as a single GPU kernel and keeping the OpenMP parallelization for the optimization algorithm, thus leading to a hybrid multi-GPU implementation of the calibrator. All these implementations have been tested with real market data for European and barrier options in the context of foreign exchange markets.

A Solution of Implicit Model of Series-Parallel Photovoltaic Arrays by Using Deterministic and Metaheuristic Global Optimization Algorithms

The implicit model of photovoltaic (PV) arrays in series-parallel (SP) configuration does not require the LambertW function, since it uses the single-diode model, to represent each submodule, and the implicit current-voltage relationship to construct systems of nonlinear equations that describe the electrical behavior of a PV generator. However, the implicit model does not analyze different solution methods to reduce computation time. This paper formulates the solution of the implicit model of SP arrays as an optimization problem with restrictions for all the variables, i.e., submodules voltages, blocking diode voltage, and strings currents. Such an optimization problem is solved by using two deterministic (Trust-Region Dogleg and Levenberg Marquard) and two metaheuristics (Weighted Differential Evolution and Symbiotic Organism Search) optimization algorithms to reproduce the current–voltage (I–V) curves of small, medium, and large generators operating under homogeneous and non-homogeneous conditions. The performance of all optimization algorithms is evaluated with simulations and experiments. Simulation results indicate that both deterministic optimization algorithms correctly reproduce I–V curves in all the cases; nevertheless, the two metaheuristic optimization methods only reproduce the I–V curves for small generators, but not for medium and large generators. Finally, experimental results confirm the simulation results for small arrays and validate the reference model used in the simulations.

Wildebeest herd optimization: A new global optimization algorithm inspired by wildebeest herding behaviour

This paper proposes a new metaheuristic global optimization algorithm inspired by Wildebeest herding behavior called Wildebeest Herd Optimization (WHO) algorithm. WHO algorithm mimics the way nomadic Wildebeest herds search vast areas of grasslands efficiently for regions of high food density. The WHO algorithm models five principal Wildebeest behaviors: firstly Wildebeests have limited eyesight and can only search for food locally, secondly Wildebeests stick to the herd to escape predators, thirdly Wildebeest herd as a whole migrates to regions of high food availability based on historical knowledge of annual grass growth rates and rainfall patterns, fourthly Wildebeests move out of crowded overgrazed regions and finally Wildebeests move to avoid starvation. The WHO algorithm is compared to Physics inspired, Swarm based, Biologically inspired and Evolution inspired global optimization algorithms on an extended test suite of benchmark optimization problems including rotated, shifted, noisy and high dimensional problems. Extensive simulation results indicate that the WHO algorithm proposed in this paper significantly outperforms state-of-the-art popular metaheuristic optimization algorithms like Particle Swarm Optimization Algorithm (PSO), Genetic Algorithm (GA), Gravitational Search Algorithm (GSA), Artificial Bee Colony Algorithm (ABC) and Simulated Annealing (SA) on shifted, high dimensional and large search range problems.

Diagnostic Gene Biomarker Selection for Alzheimer’s Classification using Machine Learning

Alzheimer’s disease (AD), also referred to as Alzheimer’s is a neurodegenerative disease and most common type of dementia. It starts at an older age and slowly progressive over time. It is a brain disease which causes loss of memory, reasoning and thinking capability of a person. Short-term memory loss is one of the main symptoms of the AD. Other common symptoms are said to be mood-swings, difficulty in understanding language and its interpretation etc. The major problem in the AD is, it can’t be reverted, but controllable with proper treatment. Genetic factors have a high impact on developing an AD, which can be inherited through genes. According to recent studies, gene therapy shows better results for Alzheimer’s patients than other common medications. It reduces the risk effect of the AD and has a gradual improvement on the patient’s condition. So, identification of gene biomarkers, having high involvement in developing AD could improve positive response over the treatment. In this paper, gene expressions of AD patients and normal peoples are analyzed using statistical approaches and Machine Learning (ML) algorithms. Differential Gene Expression (DEG) identification has an important part in the selection of most informative genes. Potential gene biomarkers are selected using a meta-heuristic global optimization algorithm called Rhinoceros Search Algorithm (RSA). As an outcome from RSA, 24 novel gene biomarkers are identified. Four supervised ML algorithms such as Support Vector Machines (SVM), Random Forest (RF), Naïve Bayes (NB) and Multilayered Perceptron Neural Network (MLP-NN) are used for the classification of two different group of samples. Among them, RSA-MLP-NN model achieved 100% accuracy on identifying the distinction between AD and normal genes and proved its efficacy.

Advanced modeling and control of 5 MW wind turbine using global optimization algorithms

This article presents a methodological approach for controller gain tuning of wind turbines using global optimization algorithms. For this purpose, the wind turbine structural and aerodynamic modeling are first described and a complete model for a 5 MW wind turbine is developed as a case study based on a systematic modeling approach. The turbine control requirements are then described and classified using its power curve to generate an appropriate control structure for satisfying all turbine control modes simultaneously. Next, the controller gain tuning procedure is formulated as an engineering optimization problem where the command tracking error and minimum response time are defined as objective function indices and physical limitations (overspeed and oscillatory response) are considered as penalty functions. Taking the nonlinear nature of the turbine model and its controller into account, two meta-heuristic global optimization algorithms (Imperialist Competitive Algorithm and Differential Evolution) are used to deal with the defined objective functions where the mechanism of interaction between the defined problem and the used algorithms are presented in a flowchart feature. The results confirm that the proposed approach is satisfactory and both algorithms are able to achieve the optimized controller for the wind turbine.