A Comparison of Metaheuristic Algorithms for Structural Optimization: Performance and Efficiency Analysis

Besides chemical composition, microstructure plays a key role to control the properties of engineering materials. A strong correlation exists between microstructure and many mechanical and physical properties of a metal. It has the utmost importance to understand the microstructure and distinguish the microstructure accurately for the appropriate selection of engineering materials in product fabrication. Computer vision and machine learning play a major role to extract the feature and predict the most probable class of a 7-class microstructural image with a high degree of accuracy. Features contain information about the image, and the classification function is defined in terms of features. Feature selection plays an important role in the classification problem to improve the classification accuracy and also to reduce the computational time by eliminating redundant or non-influential features. The current research aims at classifying microstructure image datasets by an improved wrapper-filter based feature selection method using texture-based feature descriptor. Before applying the feature selection method, a feature descriptor, called rotational local tetra pattern (RLTrP), is applied to extract the features from the input images. Then, an ensemble of three filter methods is developed by considering the union of the top-n features selected by Chi-square, Fisher score, and Gini impurity-based filter methods. The objective of this ensemble is to combine all possible important features selected by three filter methods which will be used to create an initial population of the wrapper-based meta-heuristic feature selection algorithm called, harmony search (HS). The novelty of this HS method lies in the objective function, which is defined as a function of Pearson correlation coefficient and mutual information to calculate the fitness value. The proposed method not only optimizes features with reduced dimension but also improves the performance of classification accuracy of the 7-class microstructural images. Moreover, the proposed HS model has also been compared with some standard optimization algorithms like whale optimization algorithm, particle swarm optimization, and Grey wolf optimization on the present dataset, and in every case, the HS method ensures better agreement between feature selection and classification accuracy than the other methods.

PurposeSea Lion Optimization (SLnO) algorithm involves the ability of exploration and exploitation phases, and it is able to solve combinatorial optimization problems. For these reasons, it is considered a global optimizer. The scheduling operation is completed by imitating the hunting behavior of sea lions.Design/methodology/approachCloud computing (CC) is a type of distributed computing, contributory in a massive number of available resources and demands, and its goal is sharing the resources as services over the internet. Because of the optimal using of these services is everlasting challenge, the issue of task scheduling in CC is significant. In this paper, a task scheduling technique for CC based on SLnO and multiple-objective model are proposed. It enables decreasing in overall completion time, cost and power consumption; and maximizes the resources utilization. The simulation results on the tested data illustrated that the SLnO scheduler performed better performance than other state-of-the-art schedulers in terms of makespan, cost, energy consumption, resources utilization and degree of imbalance.FindingsThe performance of the SLnO, Vocalization of Whale Optimization Algorithm (VWOA), Whale Optimization Algorithm (WOA), Grey Wolf Optimization (GWO) and Round Robin (RR) algorithms for 100, 200, 300, 400 and 500 independent cloud tasks on 8, 16 and 32 VMs was evaluated. The results show that SLnO algorithm has better performance than VWOA, WOA, GWO and RR in terms of makespan and imbalance degree. In addition, SLnO exhausts less power than VWOA, WOA, GWO and RR. More precisely, SLnO conserves 5.6, 21.96, 22.7 and 73.98% energy compared to VWOA, WOA, GWO and RR mechanisms, respectively. On the other hand, SLnO algorithm shows better performance than the VWOA and other algorithms. The SLnO algorithm's overall execution cost of scheduling the cloud tasks is minimized by 20.62, 39.9, 42.44 and 46.9% compared with VWOA, WOA, GWO and RR algorithms, respectively. Finally, the SLnO algorithm's average resource utilization is increased by 6, 10, 11.8 and 31.8% compared with those of VWOA, WOA, GWO and RR mechanisms, respectively.Originality/valueTo the best of the authors’ knowledge, this work is original and has not been published elsewhere, nor is it currently under consideration for publication elsewhere.

The inherent variability and uncertainty of solar radiation, influenced by factors such as seasons, weather, and cloud cover, pose significant challenges in accurately forecasting energy output from solar power systems. Robust forecasting models that can capture the dynamic behavior of solar radiation are crucial for optimizing energy generation, distribution, and storage processes. This study proposes an integrated approach that combines neural networks, metaheuristic optimization algorithms, and principal component analysis (PCA) for precise solar radiation forecasting. The methodology uses one year of solar radiation data from November 2022 to October 2023 from Vellore district, India. PCA is employed for dimensionality reduction, mitigating the curse of dimensionality. The reduced dataset is segregated into winter, summer, and monsoon subsets to capture distinct seasonal patterns. Neural networks are trained on these seasonal datasets, with their weights optimized by metaheuristic algorithms, including particle swarm optimization (PSO), gray wolf optimization (GWO), whale optimization algorithm (WOA), and coati optimization algorithm (COA). A comprehensive comparative study evaluated the performance of ANN-PSO, ANFIS-GWO, ANN-WOA, ANN-COA, and traditional ANN models. The ANN-COA model, integrating ANN with COA for weight optimization and PCA for dimensionality reduction, achieved the most accurate forecasts, outperforming other approaches. It attained optimal RMSE of 12.06 for winter, 6.72 for summer, and 9.49 for monsoon season, MAE of 6.66, 5.50, and 6.25, MAPE of 3.58 %, 1.68 %, and 2.31 %, and R2 of 0.973, 0.982, and 0.985 for winter, summer, and monsoon seasons respectively, demonstrating its robustness and suitability for time-series solar radiation forecasting.

Optimization algorithms are one of the effective stochastic methods in solving optimization problems. In this paper, a new swarm-based algorithm called Northern Goshawk Optimization (NGO) algorithm is presented that simulates the behavior of northern goshawk during prey hunting. This hunting strategy includes two phases of prey identification and the tail and chase process. The various steps of the proposed NGO algorithm are described and then its mathematical modeling is presented for use in solving optimization problems. The ability of NGO to solve optimization problems is evaluated on sixty-eight different objective functions. To analyze the quality of the results, the proposed NGO algorithm is compared with eight well-known algorithms, particle swarm optimization, genetic algorithm, teaching-learning based optimization, gravitational search algorithm, grey wolf optimizer, whale optimization algorithm, tunicate swarm algorithm, and marine predators algorithm. In addition, for further analysis, the proposed algorithm is also employed to solve four engineering design problems. The results of simulations and experiments show that the proposed NGO algorithm, by creating a proper balance between exploration and exploitation, has an effective performance in solving optimization problems and is much more competitive than similar algorithms.

Henry gas solubility optimization: A novel physics-based algorithm

This article presents a novel hybrid algorithm based on the grey-wolf optimizer and whale optimization algorithm, referred here as grey-wolf optimizer–whale optimization algorithm, for the dynamic parametric modelling of a four degree-of-freedom master–slave robot manipulator system. The first part of this work consists of testing the feasibility of the grey-wolf optimizer–whale optimization algorithm by comparing its performance with a grey-wolf optimizer, whale optimization algorithm and particle swarm optimization using 10 benchmark functions. The grey-wolf optimizer–whale optimization algorithm is then used for the model identification of an experimental master–slave robot manipulator system using the autoregressive moving average with exogenous inputs model structure. Obtained results demonstrate that the hybrid algorithm is effective and can be a suitable substitute to solve the parameter identification problem of robot models.

Finding a suitable solution to an optimization problem designed in science is a major challenge. Therefore, these must be addressed utilizing proper approaches. Based on a random search space, optimization algorithms can find acceptable solutions to problems. Archery Algorithm (AA) is a new stochastic approach for addressing optimization problems that is discussed in this study. The fundamental idea of developing the suggested AA is to imitate the archer's shooting behavior toward the target panel. The proposed algorithm updates the location of each member of the population in each dimension of the search space by a member randomly marked by the archer. The AA is mathematically described, and its capacity to solve optimization problems is evaluated on twenty-three distinct types of objective functions. Furthermore, the proposed algorithm's performance is compared vs. eight approaches, including teaching-learning based optimization, marine predators algorithm, genetic algorithm, grey wolf optimization, particle swarm optimization, whale optimization algorithm, gravitational search algorithm, and tunicate swarm algorithm. According to the simulation findings, the AA has a good capacity to tackle optimization issues in both unimodal and multimodal scenarios, and it can give adequate quasi-optimal solutions to these problems. The analysis and comparison of competing algorithms' performance with the proposed algorithm demonstrates the superiority and competitiveness of the AA.

Incremental learning relies on the availability of ample training data for novel classes, a requirement that is often unfeasible in various application scenarios, particularly when new classes are rare groups that are pricey or challenging to attain. The main focus of incremental learning is on the tricky task of continuously learning to classify new classes in incoming data with no erasing knowledge of old classes. The research intends to develop a comparative analysis of optimization algorithms in training few-shot continual learning models to conquer catastrophic forgetting. The presented mechanism integrates various steps: pre-processing and classification. Images are initially pre-processed through contrast enhancement to elevate their quality. Pre-processed outputs are then classified by employing Continually Evolved Classifiers, generated to address a matter of catastrophic forgetting. Furthermore, to further enhance performance, Serial Exponential Sand Cat Swarm optimization algorithm (SE-SCSO) is employed and compared against ten other algorithms, containing Grey Wolf Optimization (GWO) algorithm, Moth flame optimization (MFO), cuckoo Search Optimization Algorithm (CSOA), Elephant Search Algorithm (ESA), Whale Optimization Algorithm (WOA), Artificial Algae Algorithm (AAA), Cat Swarm Optimization (CSO), Fish Swarm Algorithm (FSA), Genetic Bee Colony (GBC) Algorithm, and Particle swarm optimization (PSO). From the experiment results, SE-SCSO had attained the maximum performance with an accuracy of 89.6%, specificity of 86%, precision of 83%, recall of 92.3% and f-measure of 87.4%.

Improved Raven Roosting Optimization algorithm (IRRO)

This research investigates the optimal design of finite impulse response (FIR) low-pass digital filters using metaheuristic optimization. A key challenge in FIR design with the Kaiser window lies in reducing sidelobe levels while maintaining transition sharpness and filter stability. To address this, the Kaiser window parameter β was adaptively optimized using grey wolf optimization (GWO), whale optimization algorithm (WOA), and particle swarm optimization (PSO). Thirty filters were tested with β values ranging approximately from 1 to 5 to evaluate trade-offs among the methods. The results show that GWO achieved the strongest sidelobe suppression, reaching –58.65 dB at β = 5.67, although with transition bandwidth widening up to 0.148π rad/sample. PSO provided the sharpest selectivity, producing the narrowest transition bandwidth of 0.058π rad/sample at β = 1.2284, while still improving sidelobe attenuation to –55.27 dB at β = 5.2565. WOA offered balanced performance, with sidelobe attenuation improving from –28.38 dB at β = 1.6470 to –55.07 dB at β = 5.2312, and transition bandwidths up to 0.138π rad/sample. Across all methods, main lobe widths remained stable within 0.172–0.181π, indicating reliable filter performance. Overall, the study demonstrates that metaheuristic-based optimization enables adaptive β tuning, offering distinct strengths: GWO in sidelobe suppression, PSO in transition sharpness, and WOA in coefficient stability. These results provide practical insights for selecting suitable optimization strategies in communication, audio processing, and precision measurement applications where computational efficiency is critical

In the field of asset allocation, how to balance the returns of an investment portfolio and its fluctuations is the core issue. Capital asset pricing model, arbitrage pricing theory, and Fama–French three-factor model were used to quantify the price of individual stocks and portfolios. Based on the second-order stochastic dominance rule, the higher moments of return series, the Shannon entropy, and some other actual investment constraints, we construct a multiconstraint portfolio optimization model, aiming at comprehensively weighting the returns and risk of portfolios rather than blindly maximizing its returns. Furthermore, the whale optimization algorithm based on FTSE100 index data is used to optimize the above multiconstraint portfolio optimization model, which significantly improves the rate of return of the simple diversified buy-and-hold strategy or the FTSE100 index. Furthermore, extensive experiments validate the superiority of the whale optimization algorithm over the other four swarm intelligence optimization algorithms (gray wolf optimizer, fruit fly optimization algorithm, particle swarm optimization, and firefly algorithm) through various indicators of the results, especially under harsh constraints.

This study offers a new Red Deer Algorithm (RDA) optimization technique for total harmonic distortion (THD) minimization in multilevel inverters (MLIs). RDA belongs to the class of swarm-based biologically inspired optimization methods. To test the angles determined via RDA optimization, a single-phase seven-level cascade multi-level inverter with symmetrical DC sources was used in this study. The switching angles for the optimally minimized total harmonic distortion (OMTHD) were computed via RDA optimization. RDA optimization was compared to meta-heuristic algorithms like the Improved Whale Optimization Algorithm (IWOA), Whale Optimization Algorithm (WOA), Sunflower Optimizer (SFO), Particle Swarm Optimization (PSO), Krill Herd (KH), Grey Wolf Optimizer (GWO), Galactic Swarm Optimization (GSA), Genetic Algorithm (GA), Fruit Fly Optimization Algorithm (FFO) and Artificial Greyctric Field Algorithm (AEFA). The RDA optimization outperformed the ten algorithms used as benchmark frameworks in this study as it can find angles with minimum THD for the modulation index in 0–1.

Text compression is a necessity for efficient data storage and transmission. Especially in the digital era, volumes of digital text have increased incredibly. Traditional text compression methods, including Huffman coding and Lempel-Ziv-Welch, have certain limitations regarding their adaptability and efficiency in dealing with such complexity and diversity of data. In this paper, we propose a hybrid method that combines Genetic Algorithm (GA) with Particle Swarm Optimization (PSO) to optimize the compression of text using the broad exploration capabilities of GA and fast convergence properties of PSO. The experimental results reflect that the proposed hybrid approach of GA-PSO yields much better performance in compression ratio than the standalone methods by reducing the size to about 65% while retaining integrity in the original content. The proposed method is also highly adaptable to various text forms and outperformed other state-of-the-art methods such as the Grey Wolf Optimizer, the Whale Optimization Algorithm, and the African Vulture Optimization Algorithm. These results support that the hybrid method GA-PSO seems promising for modern text compression.

This study presents a multi-objective deep learning framework that integrates Long Short Term Memory (LSTM) neural networks with four metaheuristic optimization algorithms: Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Genetic Algorithm (GA), and Non-dominated Sorting Genetic Algorithm II (NSGA-II) to optimize forecasting accuracy and model complexity for groundwater level prediction in data-scarce, hyper-arid urban aquifers. The framework was assessed using four groundwater monitoring wells in Kuwait City, where lagged groundwater data and cross-validation were employed to train the models. Results revealed that the models developed by using GA and PSO were superior to those developed using GWO and NSGA-II in terms of key statistical metrics. The proposed data-driven approach addresses key limitations of traditional physically based models, offering a flexible and computationally efficient alternative for groundwater forecasting. The framework presents a practical tool for real-time monitoring and management of groundwater resources in water-scarce regions.

The research and development of metaheuristic methods are critical issues in computer science. In the past decade, metaheuristic algorithms have been used in many engineering applications such as optimization of engineering problems, telecommunications, information security, and image processing. Many metaheuristic algorithms such as particle swarm optimization (PSO), ant colony optimization (ACO), and genetic algorithm (GA) are recently becoming very popular.There are many studies conducted in the literature on the comparison of PSO with other metaheuristic algorithms. In this chapter, various studies carried out between the years of 2010 and 2020 about the comparison of PSO with the other metaheuristic algorithms will be examined. The metaheuristic algorithms to be considered are simulated annealing (SA), genetic algorithm (GA), differential evolution (DE), ant colony optimization (ACO), artificial bee colony (ABC) algorithm, particle swarm optimization (PSO), tabu search (TS), harmony search (HS), firefly algorithm (FF), cuckoo search (CS), bat-inspired algorithm (BA), water wave optimization (WWO), clonal selection algorithm (CLONALG), chemical reaction optimization (CRO), sine cosine algorithm (SCA), glowworm swarm optimization (GSO), and grey wolf optimizer (GWO). This study aims to evaluate and analyze the covered papers according to several criteria such as (a) rates of studies according to publishing years, (b) the metaheuristic algorithms that are compared to PSO, (c) performance evaluation of compared algorithms, (d) the metaheuristic algorithms with their inspirational approaches and their initial proposed studies and years, (e) the field of subjects where the algorithms are applied in the reviewed studies, and (f) used databases in the examined studies.This study is a comprehensive literature review of the comparison of PSO with the most popular metaheuristic algorithms. The intention of this review is to be useful for researchers who want to conduct a survey on this area of the subject as this chapter will cover the essential and helpful analysis of the related research.