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This paper presents an improved arithmetic optimization algorithm that incorporates hybrid elite pool strategies to address the limitations of the arithmetic optimization algorithm (AOA). In AOA, the linear mathematical optimization acceleration (MOA) function cannot balance global exploitation and local exploration well. Therefore, the accuracy and convergence speed of the algorithm cannot be guaranteed. To improve the performance of AOA, this paper reconstructed a nonlinear MOA function, which is expected to balance the exploitation and the exploration of AOA. Furthermore, four hybrid elite pool strategies are integrated to enhance the ability to escape local optima. The proposed algorithm inherits the fast convergence of AOA and develops the performance of escaping local optima. Numerical experiment results on benchmark functions and engineering problems show that the proposed algorithm outperforms other compared meta-heuristic algorithms in terms of convergence speed and accuracy. [ABSTRACT FROM AUTHOR]

In order to quickly find an appropriate composition of services that meet the individual user's requirements in the Internet big data, this paper proposes an improved particle swarm service composition method based on prior knowledge. This method firstly mines the service composition partial segments with certain frequencies of usage from a large number of historical service composition solutions, i.e. the service pattern. While receiving the user's service composition requirement, this method uses the service pattern matching algorithm proposed in this paper to match the corresponding service patterns as a partial solution of this composition requirement. Then the method proposes an improved particle swarm algorithm for the part that do not successfully match the corresponding service patterns. This improved particle swarm algorithm has a mechanism to escape from the local optima. Finally, the method integrates the partial solutions of the two aspects into a complete solution, i.e. a complete service composition solution. This paper compares the optimality, time complexity and convergence with other related service composition optimization algorithms through simulation experiments. According to the analysis of the experimental results, the method proposed in this paper shows good performance in three aspects: optimality, time complexity and convergence. [ABSTRACT FROM AUTHOR]

The salp swarm algorithm (SSA) is a well-known optimization algorithm that is increasingly being utilized to solve many sorts of optimization problems. However, SSA may converge to sub-optimal solutions when it is applied to discrete problems such as the feature selection (FS) problem. This paper presents the enhanced opposition-based learning salp swarm algorithm (EOSSA), which is an improved SSA algorithm for solving the FS problem in intrusion detection systems (IDS). EOSSA incorporates four improvements into the original SSA algorithm. Firstly, the opposition-based learning (OBL) method is used in the initialization step of SSA to boost its population diversity. Secondly, the Elite opposition-based learning (EOBL) is used in the improvement loop of SSA to improve its exploration ability. Third, a variable neighborhood search (VNS) method is used in the improvement loop of SSA to improve its exploration mechanism to improve the local search space. Lastly, the Sigmoid binary transform function is used to convert the continuous candidate solutions produced by SSA into discrete binary solutions. EOSSA was evaluated against eighteen popular optimization algorithms (e.g., improved salp swarm algorithm based on opposition-based learning (ISSA), SSA, particle swarm algorithm (PSO), cuckoo search (CS), bat algorithm (BA), and Harris Hawk Optimization (HHO)) using eleven popular intrusion detection datasets (CICIDS2017, CSE-CIC-IDS2018, CICDDOS2019, CIRA-CIC-DoH, Intrusion detection 2018, UNSW-NB15, NSL-KDD, Phishing Legitimate, Malmem2022, IoT, and LUFlow Network) to Detect IoT Botnet Attacks. Moreover, EOSSA was compared with four machine learning algorithms (Decision Tree (DT), logistic regression (LR), Naive Bayes (NB), and K-Nearest Neighborhood (KNN)). The overall simulation results suggested that the proposed method is superior to the other algorithms in terms of the accuracy and number of selected features. The statistical analysis of the simulation results using the Friedman and Wilcoxon signed-rank test confirms the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Snake optimizer (SO) is an optimization algorithm drawn from the reproductive habits of serpents. It exhibits outstanding effectiveness in solving continuous optimization problems. However, SO may face some performance challenges related to its population diversity and early convergence behavior. In this paper, we address the challenges of SO by introducing the Hybrid snake optimizer algorithm (HSOA). HSOA is a novel approach to optimization that incorporates two new optimization techniques into the SO algorithm. First, it incorporates a new opposition-based learning technique called Oppositional-mutual learning into the initialization stage of the SO algorithm. Second, it integrates dynamic polynomial mutation, which is an intelligent mutation method, into the initialization and optimization stages of the SO algorithm. These integrated approaches aim to increase the population's diversity of SO, while improving its searchability during its optimization stage. In power systems, the economic load dispatch (ELD) is an intricate optimization problem that becomes more challenging when the restrictions of the valve point effect (VPE) are incorporated into it. ELD with VPE is non-convex, lacking smoothness, and exhibiting nonlinearity that considers operational limitations expressed as both equality and inequality constraints to generate electricity. The suggested HSOA algorithm underwent evaluation and was compared with 47 renowned optimization algorithms across five real-world ELD problems with different specifications: generators with different unit capacities, transmission losses, prohibited operation zones, and Ramp Rate restrictions. The experimental results demonstrate that HSOA produces competitive solutions for the five real-world ELD problems. In detail, HSOA achieves the top rank in three cases of ELD problems with a 3-unit generator, and it secures the second and third positions in high-dimensional ELD problems with 40-unit and 80-unit generators, respectively. The statistical tests confirm the reliability and efficiency of HSOA. In addition, the effectiveness of HSOA was evaluated using the single-objective IEEE-CEC 2014 functions and compared to the results of eight popular metaheuristic algorithms. The results demonstrate that HSOA is a competitive optimization algorithm capable of solving the functions of IEEE-CEC 2014. [ABSTRACT FROM AUTHOR]

In this paper, we study numerical solution of ruin probability of continuous time model. We develop an effective optimal adaptive particle swarm optimization-triangular neural network (PSO-TNN), which consists of three parts: particle swarm optimization algorithm (PSO) improved trigonometric function, extreme learning machine algorithm with initial conditions (IELM) and improved reduction algorithm. The results obtained that PSO-TNN is superior to triangular neural network (TNN) and physics-informed neural networks (PINNs), and PSO is superior to Aquila Optimizer (AO), Smell Agent Optimization (SAO), African vultures optimization algorithm (AVOA), Arithmetic optimization algorithm (AOA) in the optimization of neural network. Because the relationship between the number of neural networks and the mean square error is uncertain, we propose the adaptive reduction algorithm (AR). Through the comparison of numerical solutions with the analytical solutions and traditional numerical solutions, the PSO-TNN algorithm clearly reduced the mean square error and relative error. The PSO-TNN algorithm shows a clear improvement in terms of accuracy and overall efficiency. [ABSTRACT FROM AUTHOR]

Power system operators are faced with the problem of unit commitment belonging to mixed integer programming, which becomes very complicated, as units become large-scale and highly constrained. Because unit commitment problem is a binary problem with commitment and de-commitment, a discrete/binary optimization algorithm with superior performance is required. This paper proposes a novel hybrid binary bat algorithm for unit commitment problem, which consists of two process. To begin with, the proposed binary bat algorithm is applied to determining the commitment schedule of unit commitment problem. Specifically, an improved crossover operator based on exponential-logic-modulo map is proposed to enhance the convergence and maintain the diversity of populations. To prevent the algorithm from falling into a local optimum, a local mutation strategy performs local perturbation. Chaotic map is responsible for updating some parameters to increase the performance of the proposed algorithm. Furthermore, Lambda-iteration method is adopted to solve economic load dispatch in continuous space. Constraint handling is performed using the heuristic constraint produce. The effectiveness of the proposed algorithm is verified by benchmark functions and test systems. Additionally, the simulation results are compared with other well-established heuristic and binary approaches. [ABSTRACT FROM AUTHOR]

Arithmetic optimization algorithm (AOA) is a meta-heuristic optimization method based on mathematical operators proposed in recent years. Although it has good performance, it can also lead to insufficient local search ability and falling into local optima when solving complex optimization problems. In order to make up for the above shortcomings, the optimization performance of AOA is further improved. This paper proposes a hybrid algorithm based on AOA and particle swarm optimization (PSO) called HAOAPSO. Firstly, a compound opposition-based learning (COBL) strategy is introduced to broaden the scope of finding optimal solutions to help the algorithm better jump out of local optima. Secondly, PSO is combined with AOA that integrates COBL to improve the algorithm's local search ability, so as to improve the overall search efficiency of the algorithm. In addition, experiments are performed on 23 classical benchmark functions with different characteristics and five engineering design optimization problems, and the experimental results of HAOAPSO are compared with those of other well-known optimization algorithms to comprehensively evaluate the performance of the proposed algorithm. The simulation results show that HAOAPSO can provide better solutions in most cases when solving global optimization problems such as engineering, with better convergence speed and accuracy. [ABSTRACT FROM AUTHOR]

The evolutionary game theory aims to simulate different decision strategies in populations of individuals and to determine how the population evolves. Compared to strategies between two agents, such as cooperation or noncooperation, strategies on multiple agents are rather challenging and difficult to be simulated via traditional methods. Particularly, in a knowledge spillover problem (KSP), cooperation strategies among more than hundreds of individuals need to be simulated. At the same time, the brain storm optimization (BSO) algorithm, which is a data-driven and model-driven hybrid paradigm, has the potential to simulate the complex behaviors in a group of simple individuals. In this paper, a modified BSO algorithm has been used to solve KSP from the perspective of evolutionary game theory. Knowledge spillover (KS) is the sharing or exchanging of knowledge resources among individuals. Firstly, the KS and evolutionary game theory were introduced. Then, the KS model and KS optimization problems were built from the evolutionary game perspective. Lastly, the modified BSO algorithms were utilized to solve KS optimization problems. Based on the applications of BSO algorithms for KSP, the properties of different swarm optimization algorithms can be understood better. More efficient algorithms could be designed to solve different real-world evolutionary game problems. [ABSTRACT FROM AUTHOR]

This paper proposes the multi-objective variant of the recently-introduced fitness dependent optimizer (FDO). The algorithm is called a multi-objective fitness dependent optimizer (MOFDO) and is equipped with all five types of knowledge (situational, normative, topographical, domain, and historical knowledge) as in FDO. MOFDO is tested on two standard benchmarks for the performance-proof purpose: classical ZDT test functions, which is a widespread test suite that takes its name from its authors Zitzler, Deb, and Thiele, and on IEEE Congress of Evolutionary Computation benchmark (CEC-2019) multi-modal multi-objective functions. MOFDO results are compared to the latest variant of multi-objective particle swarm optimization, non-dominated sorting genetic algorithm third improvement (NSGA-III), and multi-objective dragonfly algorithm. The comparative study shows the superiority of MOFDO in most cases and comparative results in other cases. Moreover, MOFDO is used for optimizing real-world engineering problems (e.g., welded beam design problems). It is observed that the proposed algorithm successfully provides a wide variety of well-distributed feasible solutions, which enable the decision-makers to have more applicable-comfort choices to consider. [ABSTRACT FROM AUTHOR]

Aiming at the problems of high dimensionality, multi-constraint, multi-phase, non-linearity, and the fact that the established model is not easy to solve for the optimal scheduling of reservoir group flood control, this paper improved the dung beetle optimization algorithm by adopting a variety of strategies, proposes a new intelligent optimization algorithm, the multi-strategy improved dung beetle algorithm (MIDBO), and firstly applied it to the optimal scheduling of flood control in reservoir groups. In addition, this study analyzed and contrasted the MIDBO algorithm's calculation results with the examples of the particle swarm and dung beetle optimization algorithms in relation to the reservoir cluster in the middle and lower sections of the Yellow River. And the peak reduction rates of these algorithms are 52.82%, 51.48%, and 50.25% in turn. The outcomes demonstrate that the MIDBO algorithm has strong performance, fast optimization efficiency, obvious peak reduction effect, and is a feasible method to solve the optimization problem of reservoir flood control and scheduling. [ABSTRACT FROM AUTHOR]

The chimp optimization algorithm (ChOA) is a nature-inspired algorithm that imitates chimpanzees' individual intelligence and hunting behaviors. In this algorithm, the hunting process consists of four steps: driving, blocking, chasing, and attacking. Because of the novelty of ChOA, the steps of the hunting process have been modeled in the simplest possible way, leading to slow and premature convergence similar to other iterative algorithms. This paper proposes six spiral functions and introduces two novel hybrid spiral functions (SEB-ChOA) to rectify the abovementioned deficiencies. The SEB-ChOAs' performance is evaluated on 23 standard benchmarks, 20 benchmarks of IEEE CEC-2005, 10 cases of IEEE CEC06-2019 test-suite, and 12 constrained real-world engineering problems of IEEE CEC-2020. The SEB-ChOAs are compared with three groups of optimization algorithms, including Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) as the most well-known optimization algorithms, Slime Mould Algorithm (SMA), Marine Predators Algorithm (MPA), Ant Lion Optimization (ALO), Henry Gas Solubility Optimization (HGSO), as almost novel optimization algorithms, and jDE100 and DISHchain1e+12, as winners of IEEE CEC06-2019 competition, and also EBOwithCMAR and CIPDE as superior secondary optimization algorithms. The SEB-ChOAs reached the first rank among almost all benchmarks and demonstrated very competitive results compared to jDE100 and DISHchain1e+12 as the best-performing optimizers. Statistical evidence shows that the SEB-ChOA outperforms the PSO, GA, SMA, MPA, ALO, and HGSO optimizers while producing results comparable to those of the jDE100 and DISHchain1e+12 algorithms. [ABSTRACT FROM AUTHOR]

The parameters of support vector machines (SVMs) such as kernel parameters and the penalty parameter have a great influence on the accuracy and complexity of the classification models. In the past, different evolutionary optimization algorithms were employed for optimizing SVMs; in this paper, we propose a social ski-driver (SSD) optimization algorithm which is inspired from different evolutionary optimization algorithms for optimizing the parameters of SVMs, with the aim of improving the classification performance. To cope with the problem of imbalanced data which is one of the challenging problems for building robust classification models, the proposed algorithm (SSD-SVM) was enhanced to deal with imbalanced data. In this study, eight standard imbalanced datasets were used for testing our proposed algorithm. For verification, the results of the SSD-SVM algorithm are compared with grid search, which is a conventional method of searching parameter values, and particle swarm optimization (PSO). The experimental results show that the SSD-SVM algorithm is capable of finding near-optimal values of SVMs parameters. The results also demonstrated high classification performance compared to the PSO algorithm. [ABSTRACT FROM AUTHOR]

The Harris hawks optimization (HHO) algorithm is a new metaheuristic algorithm proposed in recent years. Due to the shortcomings of this algorithm in solving complex high-dimensional optimization problems with a slow convergence speed, low accuracy, and the high likelihood to fall into local optimum, a mixed Harris hawks optimization (MHHO) algorithm based on the pinhole imaging strategy is proposed, including four strategies to improve the optimization performance. Firstly, the pinhole imaging strategy is used to enable the Harris' hawks to approach the optimal solution faster and accelerate convergence. Secondly, the spiral parameter is introduced into the exploration phase to help the searching paths of the Harris' hawks more diverse and improve the global search ability of the algorithm. Finally, the greedy strategy of the aquila optimization algorithm and the position update strategy of the flower pollination optimization algorithm are embedded in the exploitation stage to make the algorithm jump out of local optimum effectively. To verify the effectiveness of the proposed MHHO algorithm, it is compared with the classical HHO algorithm and 16 other state-of-the-art algorithms, and extensively tested on 23 well-known benchmark functions, the IEEE CEC2017 test sets, and three complex constrained engineering optimization problems. The test results show that MHHO achieves the top ranking on both the benchmark functions and the CEC2017 test sets, demonstrating its superior performance in terms of faster convergence speed and higher accuracy. [ABSTRACT FROM AUTHOR]

Feature Selection (FS) is choosing a subcategory of features purposed to construct a machine learning model. Among the copious existing FS algorithms, Binary Particle Swarm Optimization Algorithm (BPSO) is prevalent with applications in several domains. However, BPSO suffers from premature convergence that affects exploration, resulting in dilapidation. In this current work, we boost the exploration of BPSO, incorporating the intelligence of crows to hide their food sources from other crows and predators and maintain diversity by implementing a clustering strategy. The clustering technique guarantees that the starting population is evenly distributed over the feature space while including more promising features. Additionally, suppose a crow realizes another crow or a predator is tracking it. In that case, the crow moves randomly to evict the stalker, leading to a better exploration of unexplored regions within the search space. We named the proposed method Hybrid Particle Swarm Optimization and Crow Search Algorithm with clustering initialization strategy (HPSOCSA-CIS). To evaluate the performance of HPSOCSA-CIS, 15 standard UCI datasets are utilized, and the outcomes are compared with recently proposed hybrid and standard optimization algorithms. From observation, HPSOCSA-CIS outperforms the comparing approaches for feature selection challenges on representative datasets that fall in the three-category based on dimensions. The HPSOCSA-CIS improves performance in terms of mean classification accuracy by 8.87%, 17.5%, and 21.90% on Low, medium, and high dimensional datasets, respectively. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) equipped with visual sensors are extensively used in area coverage applications. As a UAV would only cover a fraction of the region of interest, the entire region needs to be covered by several UAVs where each UAV accomplishes its own tasks. For the covering of the target region, a working method consisting of three levels has been developed. The initial step employs the Voronoi partition technique to create a number of convex sub-polygonal areas inside the target area. In the second level, each sub-polygonal area is partitioned to provide a near-optimal collection of waypoints. At the third and final level, we find a path that visits each of the waypoints without colliding with anything and is as short as feasible. Collision due to both static and dynamic obstacles is also considered for avoidance. The first and second-level partitioning processes are carried out offline, whereas path planning is handled in real-time. Traditional methods like Particle Swarm Optimisation (PSO), Genetic Algorithm (GA), and Cuckoo Optimisation Algorithm (COA) are used to evaluate the proposed work. The evaluated result of the proposed work is compared with the existing work on area coverage in terms of the percentage of inside and outside area coverage. Also, the performance of the proposed dynamic path planning method is compared with TSP and the Improved Follow the Gap Method (FGM-I). The outcome demonstrates that the proposed work is far more effective than the existing result and is suitable for the application in issue, which involves area coverage with the presence of obstacles. [ABSTRACT FROM AUTHOR]

WSN consist of tiny sensors that are distributed over the entire network and have the capability of sensing the data, processing it, and conveying it from one node to another. The purpose of the study is to minimize the power utilization per round and elevate the network lifespan. In the present case, nature-inspired mechanisms are used to minimize the power utilization of the network. In the proposed study, the Butterfly Optimization Algorithm (BOA) is used to choose the optimal quantity of cluster heads from the dense nodes (available nodes). The parameters to be considered for the choice of the cluster head are: the remaining power of the node; distance from the other nodes in the network; distance from the base station; node centrality; and node degree. The particle swarm optimization (PSO) is used to form the cluster head by choosing certain parameters, such as distance from the cluster head and the BS. The path is chosen by means of the Ant Colony Optimization (ACO) Mechanism. The route is optimized by the distance, node degree, and the chosen remaining power. The inclusive performance of the projected protocol is measured in terms of stability period, quantity of active nodes, data acknowledged by the base station, and overall power utilization of the network. The results of the put redirect methodology are correlated with the extant mechanisms such as LEACH, DEEC, DDEEC, and EDEEC (Khan et al. in World Appl Sci J, 2013; Arora and Singh in Soft Comput 23:715–734, 2019; Saini and Sharma in 2010 First international conference on parallel, distributed and grid computing (PDGC 2010), 2010; Elbhiri et al. in 2010 5th International symposium on I/V communications and mobile network, 2010) and correlated with the swarm mechanisms such as CRHS, BERA, FUCHAR, ALOC, CPSO, and FLION. This review will help investigators discover the applications in this arena. [ABSTRACT FROM AUTHOR]

Numerical simulation of proton-exchange membrane fuel cells (PEMFCs) requires an adequate model and precise parameters for reproducing their operational performance quantified by the polarization curve. Bioinspired algorithms are well-suited for optimization. The simulator is stressed by inputting thousands of randomly generated parameters, and hence, a robust numerical model is required. Once the proper model and parameters reproduce the experimental data, they can be used for design improvement. This article proposes a reformulation of a macrohomogeneous mathematical model to provide higher numerical stability to the solutions. We introduce optimization problems for parameter estimation and design optimization by applying three bioinspired algorithms to maximize its performance and minimize the platinum mass loading m Pt . The results are validated by comparing the experimental polarization curves with those simulated from the estimated parameters. We compare a base design's performance with the optimized design for maximum performance. We also compare a base design with the optimized design for minimum m Pt . The results show that the particle swarm optimization requires the lowest computational cost and performs the best in most cases, fitting the experimental data with errors lesser than 10 - 17 . The minimization of m Pt reduces the amount by 42% compared to the base case. [ABSTRACT FROM AUTHOR]

Simultaneously estimating plasma parameters of the ionosphere presents a problem for the incoherent scatter radar (ISR) technique at altitudes between ~ 130 and ~ 300 km. Different mixtures of ion concentrations and temperatures generate almost identical backscattered signals, hindering the discrimination between plasma parameters. This temperature–ion composition ambiguity problem is commonly solved either by using models of ionospheric parameters or by the addition of parameters determined from the plasma line of the radar. Some studies demonstrated that it is also possible to unambiguously estimate ISR signals with very low signal fluctuation using the most frequently used non-linear least squares (NLLS) fitting algorithm. In a previous study, the unambiguous estimation performance of the particle swarm optimization (PSO) algorithm was evaluated, outperforming the standard NLLS algorithm fitting signals with very small fluctuations. Nevertheless, this study considered a confined search range of plasma parameters assuming a priori knowledge of the behavior of the ion composition and signals with very large SNR obtained at the Arecibo Observatory, which are not commonly feasible at other ISR facilities worldwide. In the present study, we conduct Monte Carlo simulations of PSO fittings to evaluate the performance of this algorithm at different signal fluctuation levels. We also determine the effect of adding different combinations of parameters known from the plasma line, different search ranges, and internal configurations of PSO parameters. Results suggest that similar performances are obtained by PSO and NLLS algorithms, but PSO has much larger computational requirements. The PSO algorithm obtains much lower convergences when no a priori information is provided. The a priori knowledge of Ne and T e / T i parameters shows better convergences and "correct" estimations. Also, results demonstrate that the addition of N e and T e parameters provides the most information to solve the ambiguity problem using both optimization algorithms. [ABSTRACT FROM AUTHOR]