Swarm Optimization-based Algorithm Research Articles

Optimization of LightGBM for Song Suggestion Based on Users’ Preferences

Undoubtedly, music possesses the transformative ability to instantly influence an individual's mood. In the era of the incessant flow of substantial data, novel music compositions surface on an hourly basis. It is impossible to know for an individual whether he/she will like the song or not before listening. Moreover, an individual cannot keep up with this flow. However, with the help of Machine Learning (ML) techniques, this process can be eased. In this study, a novel dataset is presented, and song suggestion problem was treated as a binary classification problem. Unlike other datasets, the presented dataset is solely based on users' preferences, indicating the likeness of a song as specified by the user. The LightGBM algorithm, along with two other ML algorithms, Extra Tree and Random Forest, is selected for comparison. These algorithms were optimized using three swarm-based optimization algorithms: Grey Wolf, Whale, and Particle Swarm optimizers. Results indicated that the attributes of the new dataset effectively discriminated the likeness of songs. Furthermore, the LightGBM algorithm demonstrated superior performance compared to the other ML algorithms employed in this study.

Intelligent Health Care Data Analysis System Using Swarm based Optimization Algorithm

Large amounts of clinical data present both opportunities and challenges in modern healthcare. Effective data analysis improves patient care, early disease detection, and personalised treatment. This paper proposes an Intelligent Health Care Data Analysis System employing PSO and neural networks to improve disease detection. Missing values, normalisation, and feature selection ensure high-quality analysis inputs. Disease classification and prediction from clinical data are improved by PSO optimising neural network weights and biases. This method works on Hepatitis, Wisconsin Breast Cancer, and Cleveland Heart Disease datasets with and without missing values. PSO-optimized neural networks outperform regular neural networks in accuracy and robustness. Scalable and adaptable for real-time diagnostic applications, this intelligent system may improve healthcare delivery by enabling more precise and timely interventions. Using neural networks and advanced swarm-based optimisation, medical data analytics has improved patient outcomes and healthcare system efficiency. Particle Swarm Optimization-based Neural Network (IPSONN) diagnoses clinical illnesses better. IPSONN was created using K-means clustering and IPSO-trained Neural Networks. K-means clusterings optimal cluster number determines the hidden layers neurones. Cluster approximation error determines best clusters. Mean square error calculates hidden and output layer weights in QPSO. The classifier was tested with four clinical datasets from the University of California, Irvine (UCI) machine learning repository: Pima Indian Diabetes, Hepatitis, Wisconsin Breast Cancer, and Cleveland Heart Disease.

Chaotic RIME optimization algorithm with adaptive mutualism for feature selection problems

The RIME optimization algorithm is a newly developed physics-based optimization algorithm used for solving optimization problems. The RIME algorithm proved high-performing in various fields and domains, providing a high-performance solution. Nevertheless, like many swarm-based optimization algorithms, RIME suffers from many limitations, including the exploration-exploitation balance not being well balanced. In addition, the likelihood of falling into local optimal solutions is high, and the convergence speed still needs some work. Hence, there is room for enhancement in the search mechanism so that various search agents can discover new solutions. The authors suggest an adaptive chaotic version of the RIME algorithm named ACRIME, which incorporates four main improvements, including an intelligent population initialization using chaotic maps, a novel adaptive modified Symbiotic Organism Search (SOS) mutualism phase, a novel mixed mutation strategy, and the utilization of restart strategy. The main goal of these improvements is to improve the variety of the population, achieve a better balance between exploration and exploitation, and improve RIME's local and global search abilities. The study assesses the effectiveness of ACRIME by using the standard benchmark functions of the CEC2005 and CEC2019 benchmarks. The proposed ACRIME is also applied as a feature selection to fourteen various datasets to test its applicability to real-world problems. Besides, the ACRIME algorithm is applied to the COVID-19 classification real problem to test its applicability and performance further. The suggested algorithm is compared to other sophisticated classical and advanced metaheuristics, and its performance is assessed using statistical tests such as Wilcoxon rank-sum and Friedman rank tests. The study demonstrates that ACRIME exhibits a high level of competitiveness and often outperforms competing algorithms. It discovers the optimal subset of features, enhancing the accuracy of classification and minimizing the number of features employed. This study primarily focuses on enhancing the equilibrium between exploration and exploitation, extending the scope of local search.

Design cost minimization of a reinforced concrete column section using overnew swarm-based optimization algorithms

It is very tiresome for a practiser to detect the best feasible sizing design of structural members including reinforced concrete columns that is a highly nonlinear and complicated structural engineering optimization problem. This is due to such a design is practically conducted via conventional trial-and-error computing methods in which resistance to external loads, cost efficiency, and aesthetic factors, etc. have to be considered. This study focuses on minimizing the design cost of primarily proposed reinforced concrete column design problem via three overnew swarm-based optimizers such as Coati Optimization Algorithm, Fox Optimizer and Pelican Optimization Algorithm (POA) that are firstly utilized for this purpose. In this regard, the type of steel rebar distribution, the characteristic strength of the concrete, the height and width of the column section, and the number and diameter of the rebars are treated as discrete design variables of the newly proposed complex reinforced concrete column design cost optimization problem. In solution, the design requirements specified in practice code provisions should also be met. Here, Turkish Building Earthquake Code 2018 specifications are considered as practice structural design constraints. Consequently, the algorithmic performances of three overnew swarm-based metaheuristic optimization algorithms are compared and evaluated in detail. Amongst them, the POA shows most fruitful algorithmic design solution performance.

Gas engine CCHP system optimization: An energy, exergy, economic, and environment analysis and optimization based on developed northern goshawk optimization algorithm

This paper aims to enhance the design and operation of a Combined Cooling, Heating, and Power (CCHP) system utilizing a gas engine as the primary energy source for a residential building in China. An Energy, Exergy, Economic, and Environment (4E) analysis is employed to assess the system's performance and impact based on energy, exergy, economic, and environmental criteria. The effectiveness of the DNGO algorithm is evaluated on a case study site and compared with Northern Goshawk Optimization (NGO) and Genetic Algorithm (GA). The findings demonstrate that the DNGO algorithm identifies the optimal gas engine size of 130 kW. The algorithm's search capabilities are greatly enhanced by this unique blend, surpassing what traditional methods can offer. The DNGO algorithm brings several advantages, including unparalleled energy efficiency, reduced exergy destruction, and a substantial decrease in CO2 emissions. This not only supports environmental sustainability but also aligns with global standards. Economically, the algorithm enhances the performance of the CCHP system, evident through a reduced payback period and increased annual profit. Additionally, the algorithm's rapid convergence rate allows it to reach the optimal solution faster than its counterparts, making it advantageous for time-sensitive applications. Incorporating innovative methods like chaos theory, the DNGO algorithm effectively avoids local optima, enabling a broader search for the best solution. The utilization of Lévy flight further enhances the algorithm's ability to escape local optima and navigate the search space more efficiently. Additionally, swarm intelligence is employed to simulate the collective behavior of decentralized systems, aiding in problem-solving. This research represents a significant advancement in optimization techniques for CCHP systems and offers a fresh perspective to the field of swarm-based optimization algorithms.

Swarm based Optimization Algorithms for Task Allocation in Multi Robot Systems: A Comprehensive Review

Multi-robot systems (MRS) have gained significant attention due to their potential applications in various domains such as search and rescue, surveillance, and exploration. An essential aspect of MRS is task allocation, which involves distributing tasks among robots efficiently to achieve collective objectives. Swarm-based optimization algorithms have emerged as effective approaches for task allocation in MRS, leveraging principles inspired by natural swarms to coordinate the actions of multiple robots. This paper provides a comprehensive review of swarm-based optimization algorithms for task allocation in MRS, highlighting their principles, advantages, challenges, and applications. The discussion encompasses key algorithmic approaches, including ant colony optimization, particle swarm optimization, and artificial bee colony optimization, along with recent advancements and future research directions in this field.

An Efficient Binary Hybrid Equilibrium Algorithm for Binary Optimization Problems: Analysis, Validation, and Case Studies

Binary optimization problems belong to the NP-hard class because their solutions are hard to find in a known time. The traditional techniques could not be applied to tackle those problems because the computational cost required by them increases exponentially with increasing the dimensions of the optimization problems. Therefore, over the last few years, researchers have paid attention to the metaheuristic algorithms for tackling those problems in an acceptable time. But unfortunately, those algorithms still suffer from not being able to avert local minima, a lack of population diversity, and low convergence speed. As a result, this paper presents a new binary optimization technique based on integrating the equilibrium optimizer (EO) with a new local search operator, which effectively integrates the single crossover, uniform crossover, mutation operator, flipping operator, and swapping operator to improve its exploration and exploitation operators. In a more general sense, this local search operator is based on two folds: the first fold borrows the single-point crossover and uniform crossover to accelerate the convergence speed, in addition to avoiding falling into local minima using the mutation strategy; the second fold is based on applying two different mutation operators on the best-so-far solution in the hope of finding a better solution: the first operator is the flip mutation operator to flip a bit selected randomly from the given solution, and the second operator is the swap mutation operator to swap two unique positions selected randomly from the given solution. This variant is called a binary hybrid equilibrium optimizer (BHEO) and is applied to three common binary optimization problems: 0–1 knapsack, feature selection, and the Merkle–Hellman knapsack cryptosystem (MHKC) to investigate its effectiveness. The experimental findings of BHEO are compared with those of the classical algorithm and six other well-established evolutionary and swarm-based optimization algorithms. From those findings, it is concluded that BHEO is a strong alternative to tackle binary optimization problems. Quantatively, BHEO could reach an average fitness of 0.090737884 for the feature section problem and an average difference from the optimal profits for some used Knapsack problems of 2.482.

Elk herd optimizer: a novel nature-inspired metaheuristic algorithm

This paper proposes a novel nature-inspired swarm-based optimization algorithm called elk herd optimizer (EHO). It is inspired by the breeding process of the elk herd. Elks have two main breeding seasons: rutting and calving. In the rutting season, the elk herd splits into different families of various sizes. This division is based on fighting for dominance between bulls, where the stronger bull can form a family with large numbers of harems. In the calving season, each family breeds new calves from its bull and harems. This inspiration is set in an optimization context where the optimization loop consists of three operators: rutting season, calving season, and selection season. During the selection season, all families are merged, including bulls, harems, and calves. The fittest elk herd will be selected for use in the upcoming rutting and calving seasons. In simple words, EHO divides the population into a set of groups, each with one leader and several followers in the rutting season. The number of followers is determined based on the fitness value of its leader group. Each group will generate new solutions based on its leader and followers in the calving season. The members of all groups including leaders, followers, and new solutions are combined and the fittest population is selected in the selection season. The performance of EHO is assessed using 29 benchmark optimization problems utilized in the CEC-2017 special sessions on real-parameter optimization and four traditional real-world engineering design problems. The comparative results were conducted against ten well-established metaheuristic algorithms and showed that the proposed EHO yielded the best results for almost all the benchmark functions used. Statistical testing using Friedman’s test post-hocked by Holm’s test function confirms the superiority of the proposed EHO when compared to other methods. In a nutshell, EHO is an efficient nature-inspired swarm-based optimization algorithm that can be used to tackle several optimization problems.

Classifying collisions in road accidents using XGBOOST, CATBOOST and SALP SWARM based optimization algorithms

Classifying collisions in road accidents using XGBOOST, CATBOOST and SALP SWARM based optimization algorithms

Enhancing predictions of blast-induced ground vibration in open-pit mines: Comparing swarm-based optimization algorithms to optimize self-organizing neural networks

The objective of this paper is to present a method for predicting blast-induced ground vibration in open-pit mines that is based on the use of self-organizing neural networks (SONIA) and metaheuristic algorithms. In order to improve the accuracy of the SONIA model, several metaheuristic algorithms were employed, including the Manta Ray Foraging Optimization (MRFO), Hunger Games Search (HGS), Aquila Optimization (AO), and Naked Mole-Rat Algorithm (NMRA). Additionally, the k-fold cross-validation technique was used to identify the best parameters for the algorithms, which were then used to retrain the models for predicting blast-induced ground vibration. The effectiveness of the proposed method was evaluated using a case study of an open-pit coal mine in Vietnam, which had 288 blasting events. The study found that SONIA was a suitable neural network for predicting blast-induced ground vibration due to its self-organizing structure, even with a small dataset containing complex relationships. However, the SONIA model could be further optimized using the metaheuristic algorithms to improve its accuracy. The study found that the MRFO-SONIA model was the most reliable and accurate, with the lowest error (MAE = 0.379, RMSE = 0.453, MAPE = 0.08) and the highest reliability (R2 = 0.896). Meanwhile, the HGS-SONIA, AO-SONIA, and NMRA-SONIA models provided lower performance, with MAE values of 0.455, 0.500, and 0.492, RMSE values of 0.552, 0.603, and 0.580, MAPE values of 0.100, 0.112, and 0.111, and R2 values of 0.845, 0.815, and 0.829, respectively. The results of this study demonstrated the potential of using metaheuristic-based SONIA models to enhance predictions of blast-induced ground vibration in open-pit mines. This approach could prove useful in other operations in open-pit mines where there is a need to predict vibrations or other disturbances/negative effects resulting from specific mining activities.

COMBINING GREY WOLF OPTIMIZATION AND WHALE OPTIMIZATION ALGORITHM FOR BENCHMARK TEST FUNCTIONS

Many optimization problems have been successfully addressed using metaheuristic approaches. These approaches are frequently able to choose the best answer fast and effectively. Recently, the use of swarm-based optimization algorithms, a kind of metaheuristic approach, has become more common. In this study, a hybrid swarm-based optimization method called WOAGWO is proposed by combining the Whale Optimization Algorithm (WOA) and Grey Wolf Optimization (GWO). This method aims to realize a more effective hybrid algorithm by using the positive aspects of the two algorithms. 23 benchmark test functions were utilized to assess the WOAGWO. By running the proposed approach 30 times, the mean fitness and standard deviation values were computed. These results were compared to WOA, GWO, Ant Lion Optimization algorithm (ALO), Particle Swarm Optimization (PSO), and Improved ALO (IALO) in the literature. The WOAGWO algorithm, when compared to these algorithms in the literature, produced the optimal results in 5 of 7 unimodal benchmark functions, 4 of 6 multimodal benchmark functions, and 9 of 10 fixed-dimension multimodal benchmark functions. Therefore, the suggested approach generally outperforms the findings in the literature. The proposed WOAGWO seems to be promising and it has a wide range of uses.

IMPROVEMENT OF WOLF LEADER IN THE GREY WOLF OPTIMIZATION

The development of optimization algorithms attracts the attention of many analysts as it has advantages such as increasing performance, revenue, and efficiency in various fields, and reducing cost. Swarm-based optimization algorithms, which are among the meta-heuristic methods, are more commonly preferred because they are generally successful. In this study, the alpha wolf class, also called the wolf leader class, in the Grey Wolf Optimization (GWO), has been improved with the Whale Optimization Algorithm (WOA). This improved method is called ILGWO. To evaluate the ILGWO, 23 benchmark test functions, and 10 CEC2019 test functions were used. After running 30 iterations of the suggested algorithm, average fitness and standard deviation values have been acquired; these findings have been compared to the literature. Based on the literature's comparisons of the algorithms, the ILGWO algorithm has achieved the most optimal result in 5 of 7 functions for unimodal benchmark functions, 3 of 6 functions for multimodal benchmark functions, 9 of 10 functions for fixed-dimension multimodal benchmark functions, and 8 of 10 functions for CEC2019 test functions. So the proposed algorithm is generally better than the literature results. It has been found that the suggested ILGWO is encouraging and may be used in a variety of implementations.

Optimal power flow analysis including stochastic renewable energy sources using modified ant lion optimization algorithm

The optimal power flow is one of the major area in economic and efficient dispatch of electric power. This research article presents a swarm-based optimization algorithm, known as Modified Ant Lion Optimization (MALO) algorithm to solve optimal power flow (OPF) problems. A holistic approach has been used including thermal, wind, solar, and hydro power plants and results shows minimization of cost, losses, and voltage deviation that amounts for novelty of this paper. The MALO algorithm is validated on IEEE 30-bus and IEEE 57-bus systems, and the result are compared with the state-of-the-art algorithms. It is found that proposed algorithm provides better OPF solutions when compared with other mentioned algorithm namely Antlion Optimization, Graw Wolf Optimization, Salp Swarm Algorithm, and Grasshopper Optimization. The MALO algorithm is useful for electric utilities, researcher, and power system operation.

Smooth Exploration System: A novel ease-of-use and specialized module for improving exploration of whale optimization algorithm

Whale optimization algorithm (WOA) is a swarm-based optimization algorithm that has recently attracted extensive interest and attention because of its excellent exploitation ability. However, exploration process of WOA needs to be more valued and requires specialized mechanisms to maximize exploration ability. Therefore, based on the insight into the underlying logic of exploration process, a novel smooth exploration system (SES) is proposed to improve exploration process of WOA, and a variant of WOA is proposed called smooth WOA (SWOA). The SES comprises three mechanisms: unordered dimension sampling, random crossover, and sequential mutation. In detail, inspired by the sampling theory, unordered dimension sampling is used to adjust the sparsity of population and the proportion between exploration and exploitation. The random crossover and the sequential mutation complement each other to cover a vast search space. The performance of the SWOA is verified by comparing it with seven variants of WOA and six advanced algorithms based on 31 benchmark functions from CEC2015, CEC2021, and CEC2022. A total of six qualitative methods are introduced to analyze the performance of the SES comprehensively. Coupling coordination evaluation is introduced to present the synergy of mechanisms within the SES, which is an improvement of the ablation experiment and facilitates the consideration of rationality of each evolutionary strategy. Comprehensive qualitative analyses and fair comparisons demonstrate the remarkable performance of the SWOA.

Appropriate noise addition to metaheuristic algorithms can enhance their performance

Nature-inspired swarm-based algorithms are increasingly applied to tackle high-dimensional and complex optimization problems across disciplines. They are general purpose optimization algorithms, easy to implement and assumption-free. Some common drawbacks of these algorithms are their premature convergence and the solution found may not be a global optimum. We propose a general, simple and effective strategy, called heterogeneous Perturbation–Projection (HPP), to enhance an algorithm’s exploration capability so that our sufficient convergence conditions are guaranteed to hold and the algorithm converges almost surely to a global optimum. In summary, HPP applies stochastic perturbation on half of the swarm agents and then project all agents onto the set of feasible solutions. We illustrate this approach using three widely used nature-inspired swarm-based optimization algorithms: particle swarm optimization (PSO), bat algorithm (BAT) and Ant Colony Optimization for continuous domains (ACO). Extensive numerical experiments show that the three algorithms with the HPP strategy outperform the original versions with 60–80% the times with significant margins.

Optimal Synthesis of the Stephenson-II Linkage for Finger Exoskeleton Using Swarm-based Optimization Algorithms

Optimal Synthesis of the Stephenson-II Linkage for Finger Exoskeleton Using Swarm-based Optimization Algorithms

Performance of Ant Lion Optimization and Artificial Bee Colony Algorithm for Structural Health Monitoring of ASCE Benchmark Structure

Structural health Monitoring (SHM) has been a fast-moving tread for monitoring the health status of civil engineering structures. The SHM strategy involves application of different techniques involving the use of modal parameters, such as natural frequencies and mode shapes, to detect and localize the damage. Though localization of damage in structure plays an important role, damage quantification, which helps in performing repair works, is also essential. However, very few algorithms have been developed which calculates the amount of damage in structure. In view to this situation, the swarm-based optimization algorithms have been developed which can detect the damage severity in a structure. Some of the algorithms developed so far are Grey Wolf Algorithm (GWO), Artificial Bee Colony (ABC) algorithm, Firefly algorithm, Ant Lion Optimization (ALO) algorithm and others. Out of these algorithms, ALO and ABC have been used in limited number of cases for performing SHM. No real-life structures, considering the effect of noise, has been analysed using ALO and ABC. In this paper, the ALO and ABC optimization algorithm has been studied for damage analysis using an objective function based on the eigen value problem. The structure chosen for analysis is the ASCE Benchmark building which is a quarter scale model of an original building which give the real-life sense of SHM. Damage considered in the structure is of less severity occurring at multiple locations under an external noise, which is also a real-life challenge for damage analysis. The experimental results show that both ABC and ALO algorithms are able to quantify both major and minor damages in the presence of moderate amount of noise, proving the robustness of these algorithms.

Recent Advances in Butterfly Optimization Algorithm, Its Versions and Applications.

The butterfly optimization algorithm (BOA) is a recent successful metaheuristic swarm-based optimization algorithm. The BOA has attracted scholars' attention due to its extraordinary features. Such as the few adaptive parameters to handle and the high balance between exploration and exploitation. Accordingly, the BOA has been extensively adapted for various optimization problems in different domains in a short period. Therefore, this paper reviews and summarizes the recently published studies that utilized the BOA for optimization problems. Initially, introductory information about the BOA is presented to illustrate the essential foundation and its relevant optimization concepts. In addition, the BOA inspiration and its mathematical model are provided with an illustrative example to prove its high capabilities. Subsequently, all reviewed studies are classified into three main classes based on the adaptation form, including original, modified, and hybridized. The main BOA applications are also thoroughly explained. Furthermore, the BOA advantages and drawbacks in dealing with optimization problems are analyzed. Finally, the paper is summarized in conclusion with the future directions that can be investigated further.

Influencer buddy optimization: Algorithm and its application to electricity load and price forecasting problem

Swarm-based algorithms are widely accepted in different fields of engineering as they have proven themselves effective in solving real-world optimization problems. According to the “No-Free-Lunch” theorem, there is no such algorithm that can perform well in all optimization problems. This paper proposes a novel swarm-based optimization algorithm, influencer buddy optimization (IBO), that mimics human behavior in social environments. This algorithm exploits the fact that humans are influenced by a group of individuals rather than a single person. However, every individual can select a buddy from this group based on their preferences. The mathematical representation of this social human behavior is shown in this paper. The algorithm is tested on twenty-one standard benchmark functions, and twenty-four noiseless black-box optimization benchmarking (BBOB) functions to validate its effectiveness over fourteen optimization algorithms. Further, two hybrid models are proposed by combining IBO with an artificial neural network (ANN) and recurrent neural network (RNN) to solve electricity load and price forecasting problems. The results of hybrid models are compared with nine recent and popular optimization algorithms. The simulation result of hybrid models shows that ANN-IBO and RNN-IBO generate the least forecasting error in their respective classes of network architectures.

Optimization of MLP neural network for modeling flow boiling performance of Al2O3/water nanofluids in a horizontal tube

In this paper, a multilayer perceptron (MLP) artificial neural network (ANN) with a back-propagation (BP) training algorithm is applied for modeling thermophysical properties and subcooled flow boiling performance of Al2O3/water nanofluid in a horizontal tube. The influence of nanofluid concentration, heat flux, and flow rate on different thermophysical parameters, including thermal conductivity, thermal conductivity enhancement, viscosity, viscosity enhancement, and heat transfer coefficient, are investigated. Specifically, flow boiling of Al2O3/water nanofluid in a horizontal tube is modeled with the MLP neural network optimized by three novel swarm-based optimization algorithms: namely, Equilibrium Optimizer (EO), Marine Predators Algorithm (MPA), and Slime Mould Algorithm (SMA). To evaluate the effectiveness of different models, the MSE (Mean-Square Error) of the ANN model with varying optimization algorithms is calculated and compared. Additionally, the optimal network and regression values for each parameter are determined. The results show that the applied neural network and optimization algorithms could model the thermal conductivity, thermal conductivity enhancement, and viscosity better than the viscosity enhancement and heat transfer coefficient. The MSE of the best network for the thermal conductivity is 2.693 × 10−7, while the MSE of the best network for the viscosity enhancement is 0.0598. Also, the EO algorithm achieves the best optimization for the first three outputs, thermal conductivity, thermal conductivity enhancement, and viscosity. In comparison, the MPA algorithm extracts the optimal network for the other two outputs, viscosity enhancement, and heat transfer coefficient.