Gravitational Search Algorithm Optimization Research Articles

Hybrid game theoretic strategy for optimal relay selection in energy harvesting cognitive radio network

SummaryRelay selection plays a crucial role in enhancing the performance of wireless networks particularly in the context of cognitive radio (CR) systems with energy harvesters. In this paper, we propose a novel approach, namely, CGAPSO Shapley, for the best relay selection while simultaneously optimizing the parameters of signal‐to‐interference‐plus‐noise ratio (SINR), throughput, and outage probability. The CGAPSO Shapley algorithm combines the Shapley value, a cooperative game theory concept, with cellular genetic algorithm particle swarm optimization (CGAPSO) to achieve effective and efficient optimization of relay selection. The CGAPSO framework provides a hybrid structure that integrates cellular genetic algorithm (CGA) and particle swarm optimization (PSO), enabling simultaneous evolution of the population and particles within cells. The incorporation of the Shapley value and the hybrid CGAPSO framework enables effective exploration of the solution space and provides decision‐makers with comprehensive insights for relay selection. By utilizing the Shapley value, we assign weights to the relay nodes based on their contributions to the overall optimization objectives, considering their CR capabilities and energy harvesting capabilities. Some benchmark test functions are used to compare the hybrid algorithm with both the standard CGAPSO, Particle swarm optimization gravitational search algorithm (PSOGSA) and PSO algorithms in evolving best solution. The results show the hybrid algorithm possesses a better capability to escape from local optimums with faster convergence than the standard algorithms. The novel CGAPSO Shapley approach achieves an outage probability of 0.323324, marking a significant improvement of 60% over the outage probability achieved with conventional approach.

Efficient feature selection based novel clinical decision support system for glaucoma prediction from retinal fundus images

The process of feature selection (FS) is vital aspect of machine learning (ML) model's performance enhancement where the objective is the selection of the most influential subset of features. This paper suggests the Gravitational search optimization algorithm (GSOA) technique for metaheuristic-based FS. Glaucoma disease is selected as the subject of investigation as this disease is spreading worldwide at a very fast pace; 111 million instances of glaucoma are expected by 2040, up from 64 million in 2015. It causes widespread vision impairment. Optic nerve fibres can be degraded and cannot be replaced later in this disease. As a starting point, the retinal fundus images of glaucoma infected persons and healthy persons are used, and 36 features were retrieved from these images of public benchmark datasets and private dataset. Six ML models are trained for classification on the basis of the GSOA's returned subset of features. The suggested FS technique enhances classification performance with selection of most influential features. The eight statistical performance evaluating parameters along with execution time are calculated. The training and testing have been performed using a split approach (70:30), 5-fold cross validation (CV), as well as 10-fold CV. The suggested approach achieved 95.36 % accuracy. Due to its auspicious performance, doctors might use the suggested method to receive a second opinion, which would also help overburdened skilled medical practitioners and save patients from vision loss.

Textual emotion analysis using improved metaheuristics with deep learning model for intelligent systems

AbstractComplex, intelligent systems, namely social network services (SNS), internet‐of‐things (IoT), augmented reality (AR), virtual reality (VR), and so on have become a crucial part of our day‐to‐day life. The dynamics of this system alter human behaviors in various forms. Therefore, it is important to understand the fundamental dynamics of the system and what it involves for user sentiment. Emotion analysis is a commonly used and very powerful analysis methodology in data mining. It gives an outstanding option to evaluate, determine, understand, and monitor the sentiments of consumers with respect to a service or a product. This article focuses on the design of textual emotion analysis using the improved metaheuristics with deep learning (TEA‐IMWDL) model for intellectual systems. The goal of the TEA‐IMWDL technique lies in the identification and classification of emotions that exist in social media content. To attain this, the presented TEA‐IMWDL technique undergoes preprocessing to make the input data compatible with the latter stages of processing. For the word embedding process, the Fast text approach is utilized in this study. Next, the long short term memory‐recurrent neural network (LSTM‐RNN) method can be enforced for the emotion classification procedure. To boost the classification efficiency of the LSTM‐RNN model, an improved gravitational search optimization algorithm (IGSA) based hyperparameter tuning process is involved in this study. A brief set of simulation analyses of the TEA‐IMWDL technique is carried out and the results are tested on a benchmark dataset. The experimental outcomes demonstrate the enhanced performance of the TEA‐IMWDL technique over other recent approaches.

Optimal transmission voltage deviation based on Fractional Kinetic Gas Molecular Optimization Symbiotic Organism Search Algorithm for network operational stability improvement

This research applies Fractional Kinetic Gas Molecular Optimization Symbiotic Organism Search (FKGMOSOS) algorithm to optimize Total Voltage Deviation (TVD) while improving the security of an electricity transmission system. Hybrid KGMOSOS has excellent rate of convergence to optimum, but in order to avoid the possibility of getting stuck in local optimum, Fractional Differential Order (FDO) was integrated into the velocity term in the algebraic model of KGMOSOS leading to FKGMOSOS evolution. Using this algorithm, optimal TVD was accomplished by varying the levels of the grid control state variables to attain the finest set of optimal results in IEEE 30 – Bus, 13 – Variable standard network. The optimization competence of this new technique was verified for multiple runs through comparative investigation with that of Fractional Particle Swarm Optimization Gravitational Search Algorithm (FPSOGSA) for each FDO scenario. The results of simulation showed that for 250 successive iterations, FKGMOSOS outperformed FPSOGSA which also outperformed other literary techniques.

Protection coordination of DOCRs for different modes of microgrid operation

The present study is focussed on solving the relay coordination problem. Protection of microgrids is performed through the placement of directional overcurrent relays (DOCR). The efficacy of microgrid protection depends on coordination of all the DOCRs placed in the system. For this purpose, a number of optimization techniques have been employed by the power system researcher. In this paper, Grey Wolf Optimizer (GWO) is proposed for the protection coordination of DOCRs for different modes of microgrid operation. The proposed method is tested and validated on a 7-bus mesh system and a 9-bus radial test system. The proposed method has been compared with other meta-heuristic optimization approaches, namely Genetic Algorithm (GA), Artificial Bee Colony (ABC), Gravitational Search Algorithm (GSA), and Hybrid Particle Swarm Optimization Gravitational Search Algorithm (PSOGSA). Based on the obtained results, the GWO-based approach is very efficient in mitigating the problem of relay coordination as compared to other optimization techniques.

Application of convolutional neural networks based on Bayesian optimization to landslide susceptibility mapping of transmission tower foundation

The stability of tower foundation slopes is an important factor to maintain the operation of a power system. However, it is time-consuming and expensive to evaluate tower foundation slopes one by one due to the large area. The aim of this study is to investigate the performance of CNNs with different architectures and training options for transmission tower foundation landslide spatial prediction (LSP) by Bayesian optimization. Accordingly, fourteen influencing factors related to landslide evaluated by gain ratio technique are considered and 424 historical landslide locations in Luoding and Xinyi Counties (Guangdong Province, China) are randomly divided into 80% for training and 20% for testing the CNNs. The CNN performances are investigated by permutating and combining different numbers of convolutional layers, pooling layers and learning rate strategy. In 59 Bayesian optimized cases, three conclusions are drawn: (a) the CNNs yielded the best result with 3 convolution layers, (b) the CNN without a pooling layer performs best, and (c) a piece-wise decay learning rate strategy yields better performance. Meanwhile, the excellent performance of the CNN obtained by Bayesian optimization (CNNB) has also been validated by comparisons with gravitational search optimization algorithm and other landslide spatial models, which indicates that CNNB can be applied to generate the susceptibility maps for locating transmission tower foundations in high landslide susceptibility zones and reducing the impact of landslides on power supply by taking measures in advance.

Enhanced Energy Efficient with a Trust Aware in MANET for Real-Time Applications

Mobile ad hoc networks (MANETs) are subjected to attack detection for transmitting and creating new messages or existing message modifications. The attacker on another node evaluates the forging activity in the message directly or indirectly. Every node sends short packets in a MANET environment with its identifier, location on the map, and time through beacons. The attackers on the network broadcast the warning message using faked coordinates, providing the appearance of a network collision. Similarly, MANET degrades the channel utilization performance. Performance highly affects network performance through security algorithms. This paper developed a trust management technique called Enhanced Beacon Trust Management with Hybrid Optimization (EBTM-Hyopt) for efficient cluster head selection and malicious node detection. It tries to build trust among connected nodes and may improve security by requiring every participating node to develop and distribute genuine, accurate, and trustworthy material across the network. Specifically, optimized cluster head election is done periodically to reduce and balance the energy consumption to improve the lifetime network. The cluster head election optimization is based on hybridizing Particle Swarm Optimization (PSO) and Gravitational Search Optimization Algorithm (GSOA) concepts to enable and ensure reliable routing. Simulation results show that the proposed EBTM-HYOPT outperforms the state-of-the-art trust model in terms of 297.99 kbps of throughput, 46.34% of PDR, 13% of energy consumption, 165.6 kbps of packet loss, 67.49% of end-to-end delay, and 16.34% of packet length.

Artificial intelligence based medical decision support system for early and accurate breast cancer prediction

Feature selection, which picks the optimal subset of characteristics related to the target data by deleting unnecessary data, is one of the most important aspects of the machine learning area. A major part of big data preprocessing is feature selection (reduction). There are 2n alternative feature subsets for every n features, making it difficult to choose the best set of features from a dataset using typical feature selection techniques. Consequently, the present study proposes and suggests a unique feature selection method based on the Eagle Strategy(ESO) Optimization, Gravitational Search Optimization (GSO) algorithm, and their hybrid algorithm. We chose this infection as our subject of investigation since the number of women with breast cancer is increasing rapidly on a global scale. After lung cancer, which affects more women than any other kind of cancer, breast cancer is the second leading cause of cancer mortality. The goal of this study is to categorize breast cancer into two groups using the benchmark feature set (Wisconsin Diagnostic Breast Cancer (WDBC)) and to choose the fewest features (feature selection) to achieve maximum accuracy. This work also provides a hybrid technique for finding important features that combines two algorithms, ESO and the GSO algorithm, while reducing insignificant characteristics (features) and complexity. Soft computing technologies and machine learning algorithms provide a framework for prognostic research by classifying data instances as relevant or irrelevant depending on cancer severity. Thus, this work presented a new approach for classifying breast cancer tumors. In this research, we coupled soft computing methodologies—our implemented algorithms are applied for the first time to this problem—with artificial intelligence-based machine learning strategies to create a prediction model. The efficacy of our suggested technique was evaluated using WDBC breast cancer data sets, and the findings show that our proposed hybrid algorithm performs very well in breast cancer classification. We have been able to attain astonishing results with accuracy up to 98.9578%, sensitivity up to 0.9705, specificity up to 1.000, precision up to 1.000, F1-score up to 0.9696, and an AUC up to 0.9980 (close to maximum, i.e., 1.0000). Our study's goal is to incorporate our findings into a valid clinical prediction system, allowing visual science specialists to make more accurate and effective judgments in the future. Furthermore, our suggested technology might be used to detect a wide range of diseases.

Power minimization of gas transmission network in fully transient state using metaheuristic methods

Abstract In gas transmission networks, the pressure drop caused by friction is one of the main operation costs that is compensated through consuming energy in the compressors. In the competitive market of energy, considering the demand variation is inevitable. Hence, the power minimization should be carried out in transient state. Since the minimization problem is severely nonlinear and nonconvex subjected to nonlinear constraints, utilizing a powerful minimization tool with a straightforward procedure is very helpful. In this paper, a novel approach is proposed based on metaheuristic algorithms for power minimization of a gas transmission network in fully transient conditions. The metaheuristic algorithms, unlike the gradient dependent method, can solve the complicated minimization problem without simplification. In the proposed strategy, the cost function is not expressed explicitly as a function of minimization variables; therefore, the transient minimization can be as precise as possible. The minimization is carried out by a straightforward methodology in each time sample, which leads to more precise solutions as compared to the quasi transient minimization. The metaheuristic minimizer, called the particle swarm optimization gravitational search algorithm (PSOGSA), is utilized to find the optimum operating set points. The numerical results also confirm the accuracy and well efficiency of the proposed method.

Transmission Efficiency Optimal Design of Spiral Bevel Gear Based on Hybrid PSOGSA (Particle Swarm Optimization—Gravitational Search Algorithm) Method

Transmission efficiency is a significant index of the transmission system. Even though much research has been carried out to calculate gear transmission efficiency, only a few of them studied spiral bevel gear due to its complexity. Moreover, spiral bevel gear does not have a “standard surface”, which means more complex coupling relations between different parameters and makes efficiency optimal design more difficult. Therefore, an instantaneous transmission efficiency computing model of a spiral bevel gear was set up based on loaded tooth contact analysis and hybrid elasto-hydrodynamic lubrication theory. Then, the particle swarm optimization–gravitational search algorithm (PSOGSA) optimal model was constructed to obtain the best parameters that maximize the average transmission efficiency of spiral bevel gears. Control parameters and machining parameters are optimized in sequence based on the proposed optimal model. The results showed that both optimal designs could help improve transmission efficiency, but the range of machining parameters is limited in a small interval because of the complex coupling relations. Therefore, the machining parameters optimization are conducted after control parameters optimization, which showed good results. Transmission efficiency was finally improved to 98.78%, which increased more than 4% at least. The proposed optimal model could also be applied into other gear design methods or even other fields.

Fractional memetic computing paradigm for reactive power management involving wind-load chaos and uncertainties

Optimal reactive power dispatch (ORPD) with integration of renewable energy resources has a growing interest in research community due to its utmost requirements during the operation, planning and design of the modern electrical power networks. The objective of ORPD is to improve the performance of power network by means of reducing the losses in transmission line, improving the voltage profile, and decreasing the overall cost of operation through optimal tuning of the operational variables such as tap position of transformers, generator output voltages and capacitor banks. However, the nonlinear, non-stationary and complex nature of power network, presence of load uncertainties, and dynamic behavior of wind generation introduces a complex optimization task which cannot be readily solved in an efficient manner. In this research work, a new fractional memetic computing paradigm, i.e., the fractional particle swarm optimization gravitational search algorithm with entropy evolution (FPSOGSA-EE), is designed to solve the ORPD problems in power system adopting wind power plants (WPPs) and load uncertainties. The proposed optimization framework FPSOGSA-EE integrates the concept of fractional calculus and Shannon entropy to strengthen the optimization characteristics of canonical algorithm. The exhaustive experimentation endorse the efficacy of FPSOGSA-EE by providing minimum gauge of fitness evaluation function, namely, the line loss and voltage deviation index minimization, in IEEE 30 and 57 bus networks. The stability, consistency and reliability of proposed FPSOGSA-EE is ascertained through statistical interpretations by means of boxplots, probability measures for cumulative distribution function, and histogram illustrations.

The Impact of Palliative Care and Nursing Intervention on the Psychology and Quality of Life of Elderly Patients with Colorectal Cancer.

Colorectal cancer patients face physical, psychological, and social difficulties. Psychosocial therapies appear to be successful in improving cancer patients' psychological and social results. Preoperative and postoperative psychological therapies for colorectal surgery patients have not been extensively studied. During their treatment, up to 35% of cancer patients experience clinically severe psychological discomfort. As a consequence, a greater knowledge of health-related quality of life and its causes can assist oncology nurses in developing effective treatments to improve the health-related quality of life. The palliative care model and the nursing intervention model are used in this study to assess the effectiveness of an individually customized nursing intervention for lowering chemotherapy-related symptom distress in adult patients with colorectal cancer. Initially, the dataset is collected and split into the control group and the experimental group. The patient conditions are evaluated using the novel accelerated gradient boosting regression tree (AGBRT) estimation model. For improving the evaluation process, we have proposed the enriched gravitational search optimization algorithm (EGSOA). The system's success is evaluated in terms of the patients' psychological well-being and quality of life.

Levy Flight and Chaos Theory-Based Gravitational Search Algorithm for Global Optimization

The Gravitational Search Algorithm (GSA) is one of the highly regarded population-based algorithms. It has been reported that GSA has a powerful global exploration capability but suffers from the limitations of getting stuck in local optima and slow convergence speed. In order to resolve the aforementioned issues, a modified version of GSA has been proposed based on levy flight distribution and chaotic maps (LCGSA). In LCGSA, the diversification is performed by utilizing the high step size value of levy flight distribution while exploitation is carried out by chaotic maps. The LCGSA is tested on well-known 23 classical benchmark functions. Moreover, it is also applied to three constrained engineering design problems. Furthermore, the analysis of results is performed through various performance metrics like statistical measures, convergence rate, and so on. Also, a signed Wilcoxon rank-sum test has also been conducted. The simulation results indicate that LCGSA provides better results as compared to standard GSA and most of the competing algorithms.

Internet of Medical Things (IoMT) and Reflective Belief Design-Based Big Data Analytics with Convolution Neural Network-Metaheuristic Optimization Procedure (CNN-MOP).

In recent times, the Internet of Medical Things (IoMT) is a new loomed technology, which has been deliberated as a promising technology designed for various and broadly connected networks. In an intelligent healthcare system, the framework of IoMT observes the health circumstances of the patients dynamically and responds to backings their needs, which helps detect the symptoms of critical rare body conditions based on the data collected. Metaheuristic algorithms have proven effective, robust, and efficient in deciphering real-world optimization, clustering, forecasting, classification, and other engineering problems. The emergence of extraordinary, very large-scale data being generated from various sources such as the web, sensors, and social media has led the world to the era of big data. Big data poses a new contest to metaheuristic algorithms. So, this research work presents the metaheuristic optimization algorithm for big data analysis in the IoMT using gravitational search optimization algorithm (GSOA) and reflective belief network with convolutional neural networks (DBN-CNNs). Here the data optimization has been carried out using GSOA for the collected input data. The input data were collected for the diabetes prediction with cardiac risk prediction based on the damage in blood vessels and cardiac nerves. Collected data have been classified to predict abnormal and normal diabetes range, and based on this range, the risk for a cardiac attack has been predicted using SVM. The performance analysis is made to reveal that GSOA-DBN_CNN performs well in predicting diseases. The simulation results illustrate that the GSOA-DBN_CNN model used for prediction improves accuracy, precision, recall, F1-score, and PSNR.

Hybrid Particle Swarm Optimization–Gravitational Search Algorithm Based Detection of Graphene Defects With Electrical Impedance Tomography

Recently, graphene has gained a lot of attention in the electronic industry due to its unique properties and to overcome the limits of miniaturization making way for novel devices in the field of electronics. Among the synthesizing methods for growing large graphene films, chemical vapour deposition is one of the promising and common techniques but defects such as cracks, holes, or wrinkles are hard to avoid. The presence of defects influence the electrical properties of graphene thus the local conductivity distribution across the surface of the graphene can characterize its electrical behavior. The difference in conductivity values of the defect and the background can be estimated with electrical impedance tomography. Due to the very high conductivity of graphene, the reconstructed electrical impedance tomography conductivity images suffer from poor spatial resolution. Considering the graphene defect conductivity and the number of defects are known a priori then the unknowns are defect geometry and background conductivity of graphene that are estimated using hybrid particle swarm optimization - gravitational search algorithm. The defect geometries are described by truncated Fourier series coefficient which can represent the complex shapes. Numerical studies are done for graphene characterization with single and multiple defects. Monte Carlo simulations with 20 runs having different noise seed are carried out to evaluate the robustness of the proposed algorithm. Statistical analysis is done and the results of the proposed algorithm are compared against the conventional modified Newton Raphson method and gravitational search algorithm. Experimental studies with graphene sheet 2.5 cm x 2.5 cm with defects are performed for shape estimation. The results showed that the proposed algorithm has a good estimation of background conductivity and defect geometry.

A novel gaussian based particle swarm optimization gravitational search algorithm for feature selection and classification

A Gaussian based Particle Swarm Optimization Gravitational Search Algorithm (GPSOGSA) is being proposed for extensive feature selection that serves highly in making effective predictions. GPSOGSA helps to overcome the problem of being stuck into the local optima and influences the local searching ability, thus it aims to bridge the gap of exploration and exploitation. The algorithm also limits the usage of too many parameters like acceleration factors, maximum velocity, inertia weight that plays a vital role in PSO, GSA and PSOGSA. The efficacy of the algorithm has been tested upon unimodal and multimodal benchmark functions. We have also evaluated the performance of the algorithm by applying it on various benchmark datasets. The algorithm uses a wrapper-based approach that includes Support Vector Machine as a learner algorithm, and improves both the execution time and the performance accuracy. The findings show that the proposed algorithm could escape from local optimum and converges faster than the PSO, GSA and PSOGSA algorithms.

Analysis and improvement of GSA’s optimization process

Gravitational search algorithm (GSA) is one of the heuristic algorithms proposed in recent years, which is inspired by the law of universal gravitation between masses. However, many practical applications and researches show that when the region affected by global optimum occupies less search space, GSA is prone to fall into local optimum, especially when the optimal value is close to the boundary of the search space and there are sub-optimal solutions in the center of the space. By microscopic analysis of the particle motion process of GSA in the above optimization situation, we find that the Kbest mechanism and the characteristic of central convergence are the two main factors affecting the GSA optimization performance. In this paper, an improved algorithm called Balanced Gravitational Search Algorithm is proposed, in which the balance operator is designed to solve two inherent problems in GSA. Then the proposed method is firstly tested on 10 benchmark functions provided by CEC 2020 compared with the state-of-the-art variant algorithms of the GSA and other typical meta-heuristics. Further, the algorithms are tested and compared on the real-world optimization problems including CEC 2011 real-world optimization problems and the Multi-Layer Neural Network (MLNN) training problem based on wine dataset. The simulation results show that BGSA can significantly improve the optimization performance of GSA and it can be a good choice for solving real-world optimization problems.

Optimized Harmonic Reduction PWM based Control Technique for Three-Phase quasi Z-Source Inverter

This paper proposes an optimized harmonic reduction pulse width modulation (HRPWM) control strategy for three-phase quasi Z-source inverter (qZSI). In traditional sinusoidal or space vector pulse width modulation techniques, the flexibility in adjustment of individual switching angles is not possible and thus, these techniques are not optimum choices for low switching frequency operations of high/medium power qZSI. In the proposed technique, adjustments of switching angles of HRPWM waveform are possible to achieve optimum performance. The optimum performance is targeted as maximization of boosting factor and simultaneous minimization of weighted total harmonic distortion (WTHD) at the output voltage of qZSI. The hybrid particle swarm optimization gravitational search algorithm (PSOGSA) is used for computation of optimum switching angles of suggested HRPWM waveform at various modulation indices. The obtained WTHDs up to 49th order harmonics and boosting factors of optimized HRPWM methodology are compared with that of the maximum boost control (MBC) technique for qZSI to justify superior performances of the suggested method in low switching frequency range. The proposed concept has been verified via simulation study. The experimentation (qZSI controlled by microcontroller) validates the working of optimized HRPWM based qZSI which agrees with software results.

An Improved PSOGSA for Clustering and Routing in WSNs

Wireless sensor network (WSN) is an integration of sensing, communicating, computing in a board range environment. Efficient energy consumption becomes the most challenging task for sensor nodes. The clustering and routing techniques are promising methods to resolve the issue and extend the network’s lifespan. The clustering technique is defined as grouping data into classes, every cluster sharing a high degree of similarity in between them, and each cluster being dissimilar with others. This technique is the best data processing model for WSN, and it controls the redundant data inside the network. The nomination of the appropriate cluster head is a major factor in the clustering technique. The object of this proposed paper is to equipoise the energy of the clustering nodes and route the data from cluster head to sink. We propose an improved particle swarm optimization gravitational search algorithm for clustering and routing in WSNs. Here clustering algorithm makes equal energy in the entire network by the uniform distribution of cluster head, routing algorithm decides the ideal routing path to convey data packet between cluster head and sink. The proposed paper integrates the exploration capacity of GSA and the exploitation capability of PSO. Detailed simulation performs using MATLAB based simulator in terms of residual energy, network lifespan, and convergence rate. In comparing our proposed algorithm to other existing algorithms, it outperforms significantly.

A Hybrid Modified Method of the Sine Cosine Algorithm Using Latin Hypercube Sampling with the Cuckoo Search Algorithm for Optimization Problems

The metaheuristic algorithm is a popular research area for solving various optimization problems. In this study, we proposed two approaches based on the Sine Cosine Algorithm (SCA), namely, modification and hybridization. First, we attempted to solve the constraints of the original SCA by developing a modified SCA (MSCA) version with an improved identification capability of a random population using the Latin Hypercube Sampling (LHS) technique. MSCA serves to guide SCA in obtaining a better local optimum in the exploitation phase with fast convergence based on an optimum value of the solution. Second, hybridization of the MSCA (HMSCA) and the Cuckoo Search Algorithm (CSA) led to the development of the Hybrid Modified Sine Cosine Algorithm Cuckoo Search Algorithm (HMSCACSA) optimizer, which could search better optimal host nest locations in the global domain. Moreover, the HMSCACSA optimizer was validated over six classical test functions, the IEEE CEC 2017, and the IEEE CEC 2014 benchmark functions. The effectiveness of HMSCACSA was also compared with other hybrid metaheuristics such as the Particle Swarm Optimization–Grey Wolf Optimization (PSOGWO), Particle Swarm Optimization–Artificial Bee Colony (PSOABC), and Particle Swarm Optimization–Gravitational Search Algorithm (PSOGSA). In summary, the proposed HMSCACSA converged 63.89% faster and achieved a shorter Central Processing Unit (CPU) duration by a maximum of up to 43.6% compared to the other hybrid counterparts.