Benchmark Test Suite Research Articles

A novel hybrid algorithm based on improved marine predators algorithm and equilibrium optimizer for parameter extraction of solar photovoltaic models

In the field of solar photovoltaic (PV) systems, the accurate and reliable extraction of parameters from PV models is crucial for effective simulation, evaluation, and control. Although various optimization algorithms have been widely used for parameter extraction in solar PV systems, the accuracy and reliability of the parameters extracted by these methods usually fall short of the expected standards. To address these shortcomings, a novel hybrid algorithm that combines the improved marine predators algorithm (MPA) with the equilibrium optimizer (EO), named IMPAEO, is proposed. In the IMPAEO, an elite opposition-based learning strategy is introduced to expand the exploration range of the algorithm to enhance population diversity; an adaptive weight coefficient is employed to improve the updating rule of the MPA, facilitating a balance between global exploration and local exploitation; the EO operator is integrated to enhance the performance of the MPA in the local exploitation phase to improve the solution quality and convergence speed; and the linear population size reduction (LPSR) technique is introduced to dynamically reduce the population size and improve the search performance. The effectiveness of the proposed IMPAEO is validated using the CEC2017 benchmark test suite, which is also applied to extract parameters of the single diode model, the double diode model, and three different PV modules. Comparative analysis with other algorithms demonstrates that the IMPAEO exhibits significant advantages in terms of solution quality, convergence speed, and algorithm stability. Consequently, the proposed IMPAEO not only proves to be efficient and reliable in parameter extraction for solar PV models but also offers a promising alternative in this field.

Dynamic Mutation Strategy Selection in Differential Evolution Using Perturbed Adaptive Pursuit

Diverse mutant vectors play a significant role in the performance of the Differential Evolution (DE). A mutant vector is generated using a stochastic mathematical equation, known as mutation strategy. Many mutation strategies have been proposed in the literature. Utilizing multiple mutation strategies with the help of an adaptive operator selection (AOS) technique can improve the quality of the mutant vector. In this research, one popular AOS technique known as perturbation adaptive pursuit (PAP) is integrated with the DE algorithm for managing a pool of mutation strategies. A community-based reward criterion is proposed that rewards the cumulative performance of the whole population. The proposed approach is called ‘Dynamic Mutation Strategy Selection in Differential Evolution using Perturbed Adaptive Pursuit (dmss-DE-pap)’. The performance of dmss-DE-pap is evaluated over the 30D and 50D optimization problems of the CEC 2014 benchmark test suite. Results are competitive when compared with other state-of-the-art evolutionary algorithms and some recent DE variants.

Slime mould algorithm with mechanism of leadership and self-phagocytosis for multilevel thresholding of color image

Threshold based segmentation method is widely used because of its simplicity, high efficiency and easy implementation. However, as the number of thresholds increases, the algorithm's search space also increases, increasing computational complexity, which reduces computational efficiency and segmentation accuracy. Additionally, the existing multi-threshold (MT) segmentation methods still struggle with slow convergence speed and low solution accuracy. The optimum threshold for the image is determined in this study using the Slime Mould Algorithm with the Mechanism of Leadership and Self-phagocytosis (SMA-MLS). Firstly, the initial population production method based on Logistic-Tent mapping is introduced to enhance the diversity of algorithm solutions. Secondly, the position update formula with leadership mechanism is proposed to improve the convergence speed and accuracy. In addition, the adaptive combined mutation mechanism balances the exploration and exploitation ability. Finally, the self-phagocytosis mechanism maintains population diversity and heritability. A series of benchmark test suites are employed to evaluate the performance of the algorithm. Experimental results demonstrate that SMA-MLS does show good performance. It is superior in terms of convergence accuracy, convergence speed and stability. In the MT segmentation experiments, by using Kapur as the objective function and working with color images, SMA-MLS proved to be effective in the MT segmentation problem of images. Moreover, SMA-MLS will be applied to mechanical parameter optimization, neural network and other fields in future research.

Niche center identification differential evolution for multimodal optimization problems

Niching techniques are commonly incorporated into evolutionary computation (EC) algorithms to address multimodal optimization problems (MMOPs). Nevertheless, identifying proper individuals as niche centers remains the main challenge in niching techniques. Generally, niche centers should possess promising fitness (fitness aspect) and should be dispersedly distributed in different search regions (distance aspect). In this study, we propose a novel niching technique known as niche center identification (NCI) and integrate it with differential evolution (DE) for tackling MMOPs, termed NCIDE. In NCI, niche centers are first identified from both the fitness and distance aspects. Individuals that are not niche centers are added to their nearest niche centers to form niches. Moreover, we develop a niche-level archival-adaptive parameter scheme (NAAPS) to adaptively adjust the parameters at the niche level and reduce their sensitivity. Meanwhile, with the help of an archive, we can preserve the identified optima and reinitialize stagnant individuals for further exploration. The experimental results on the CEC2013 multimodal benchmark test suite demonstrate that NCIDE significantly outperforms several state-of-the-art multimodal algorithms, including multiple competition winners from CEC2015 and GECCO2017-GECCO2019. Finally, NCIDE is applied to solve multimodal nonlinear equation system (NES) problems to further illustrate its practical applicability.

A Hybrid Equilibrium Optimizer Based on Moth Flame Optimization Algorithm to Solve Global Optimization Problems

Abstract Equilibrium optimizer (EO) is a novel metaheuristic algorithm that exhibits superior performance in solving global optimization problems, but it may encounter drawbacks such as imbalance between exploration and exploitation capabilities, and tendency to fall into local optimization in tricky multimodal problems. In order to address these problems, this study proposes a novel ensemble algorithm called hybrid moth equilibrium optimizer (HMEO), leveraging both the moth flame optimization (MFO) and EO. The proposed approach first integrates the exploitation potential of EO and then introduces the exploration capability of MFO to help enhance global search, local fine-tuning, and an appropriate balance during the search process. To verify the performance of the proposed hybrid algorithm, the suggested HMEO is applied on 29 test functions of the CEC 2017 benchmark test suite. The test results of the developed method are compared with several well-known metaheuristics, including the basic EO, the basic MFO, and some popular EO and MFO variants. Friedman rank test is employed to measure the performance of the newly proposed algorithm statistically. Moreover, the introduced method has been applied to address the mobile robot path planning (MRPP) problem to investigate its problem-solving ability of real-world problems. The experimental results show that the reported HMEO algorithm is superior to the comparative approaches.

Reinforcement learning driven moth‐flame optimisation algorithm for solving numerical optimisation problems

AbstractMoth‐flame optimisation (MFO) algorithm has received a lot of attention recently, due to its simple structure and easy coding. Researchers have demonstrated that the original MFO algorithm suffers from the drawbacks of insufficient variety, slow convergence speed, and readily sliding into local optimum, which are brought about by the imbalance between local and global search. Reinforcement learning driven moth‐flame optimisation (RLMFO) algorithm is designed to correct these issues. Opposition learning is employed to broaden the variety of the initial population. Reinforcement learning is introduced to direct the local and global search process of the algorithm. A strategy pool containing Gaussian mutation (GM), Cauchy mutation (CM), Lévy mutation (LM), and elite strategy (ES) is created to hold strategies with various functions. RLMFO is verified on the benchmark test suite in CEC 2017. RLMFO performs better than cutting‐edge algorithms according to experimental findings.

Study of the Accuracy of the Ronen Method at Material Interfaces

The Ronen Method (RM) is a nonlinear iterative scheme that demands successive resolutions of the diffusion equation, where local diffusion constants are modified to reproduce more accurate estimates of the neutron currents by a transport operator. The methodology is currently formulated using the formalism of the collision probability method for evaluation of the current. The RM was recently tested on a complete suite of one-dimensional (1-D) multigroup benchmark problems. Small differences in the flux (less than 2%) were reported at material interfaces and close to the vacuum boundary with respect to the reference solution from transport. This work investigates first a possible numerical equivalence between transport and diffusion in some representative 1-D problems from the same benchmark test suite. The equivalence is sought with optimal diffusion coefficients computed using reference transport solutions that allow for adjusting the diffusion model. The RM, which attempts to obtain such equivalent diffusion coefficients without knowing the reference solution, is then compared to the optimal coefficients. The accuracy of the flux distribution at material interfaces is investigated for different approximations of the vacuum boundary and by decreasing progressively the RM convergence tolerance set in the iterative scheme. Using tighter convergence criteria, the RM calculates more accurate flux distributions at all material interfaces, regardless of the value of the diffusion coefficient and the extrapolated distance set at the beginning of the iterative scheme. Maximum flux deviations are remarkably reduced when the RM convergence tolerance is set to eight or more significant digits, leading to improvements in the flux deviation of two orders in magnitude and providing numerical proof for equivalence with transport in the tested configurations.

Adaptive knowledge transfer-based particle swarm optimization for constrained multitask optimization

Constrained multitask optimization (CMTO) problems are frequently encountered in practical applications. However, since the feasible regions of different tasks are difficult to match under constraints, it is hard to obtain and transfer effective knowledge among different tasks, resulting in poor convergence performance. To address this issue, a constrained multitask particle swarm optimization algorithm with knowledge transfer-based adaptive penalty function (CMTPSO-APF) is proposed to improve the convergence performance. The main contributions of CMTPSO-APF are three-fold. First, a dynamic particle topology is designed to improve the neighborhood similarity for different tasks with constrained conditions. Then, the particles with similar spatial structures can facilitate the knowledge transfer between different tasks. Second, a covariance matrix learning mechanism is proposed to obtain valid knowledge with constrained conditions. Then, the proposed method can promote the population to move in the direction of the optimal solution. Third, a knowledge transfer-based adaptive penalty function for constrained multitask optimization is designed to achieve the dynamical change of penalty factor according to the evolution state. Then, the adaptive penalty function can promote the transfer of effective knowledge and improve the convergence. Finally, some simulation experiments are carried out on the CMTO benchmark test suites and the practical application of the wastewater treatment process. The results indicate that CMTPSO-APF can improve the convergence performance.

Growing Neural Gas Network-based surrogate-assisted Pareto set learning for multimodal multi-objective optimization

The key issue in handling multimodal multi-objective optimization problems (MMOPs) is to find multiple Pareto sets (PSs) corresponding to one Pareto front (PF). Therefore, learning the PSs is critical to facilitate solving MMOPs while unfortunately, current research only focuses on PF learning which is helpless in finding multiple PSs by the information of one PF. Moreover, since the PSs of an MMOP are usually non-functional, traditional approximative function model-based PF learning is inapplicable. Consequently, developing new PS learning techniques is desired. Inspired by data-driven evolutionary algorithms, data can be used to train surrogate models to assist the algorithm. This article proposes an online data-driven PSs learning technique that aims to learn the topologies of PSs through a surrogate model to facilitate the search for PSs. Specifically, the Growing Neural Gas network is trained using non-dominated solutions to learn the topologies of PSs during the evolutionary process. Then, the nodes of the network are used to generate new solutions and adopted as reference points for environmental selection. A new algorithm is developed based on the PS learning technique for MMOPs. Experimental studies on three benchmark test suites and two different real-world applications demonstrate the superiority of our method over six state-of-the-art algorithms dedicated to MMOPs. The PSs learning technique can obtain the topologies of PSs and facilitate the search for them.

An operator-inspired framework for metaheuristics and its applications on job-shop scheduling problems

The job-shop scheduling problem (JSP) is a well-known combinatorial optimization problem in manufacturing systems. For the past two decades, real-number metaheuristics have been widely used to solve the JSP using the real-number transform methods. A limitation of the real-number metaheuristics is the premature convergence due to the stochasticity of the transform methods. To eliminate this limitation, a novel operator framework has been designed, building the bridge between the discrete optimization problem (JSP) and real-number metaheuristics (also called continuous metaheuristics). Specifically, this paper captures the core operators of the real-number metaheuristics, namely, addition, subtraction, and multiplication. Firstly, three new operators are reconstructed according to several simple neighborhood structures to solve the JSP robustly and effectively. The properties of the arithmetic (symmetry) are taken into account in the reconstruction of the proposed operators to avoid excessive redundant searches. Secondly, a positional similarity-based population diversity is presented for the JSP to demonstrate the intrinsic distinctions between the proposed operators and the transform methods during the evolutionary process. Finally, the results of five widely used benchmark test suites (185 instances) show that the proposed operators can achieve a better balance between exploration and exploitation than the current real-number transform methods.

Multi-objective optimization of reactive power dispatch problem using fuzzy tuned mayfly algorithm

The recent multi-objective optimization problems are very complex which require an effective and robust evolutionary method for obtaining a global Pareto-optimal solutions. Most of these multi-objective evolutionary techniques are population-based methods which usually work on random search approaches and often trapped into local minima during their execution. A new fuzzy tuned mayfly algorithm (FTMA) is presented in this paper, to obtain the Pareto-optimal solutions for any complex multi-objective power system problem. It has a self-adapting global exploration capability to get the best Pareto-optimal solutions by varying two crucial parameters i.e., crossover (Pc) and mutation Pm probabilities. Moreover, a model of Pareto-dominance is employed to rank non-dominating solutions to maintain the diversity within the populations. The proposed evolutionary approach is examined on a standard ZDT benchmark test suite and its algorithmic performance is statistically compared with a few of the recent optimization techniques such as NSGA-II, NSGA-III, MMODE, MOAVOA and MMA algorithm. It is further applied to minimize two distinct objective functions (i.e., total transmission loss and voltage stability index) for IEEE-118 bus system and IEEE-24 bus RTS. A comprehensive statistical analysis is presented for the obtained numerical results, which proves that proposed FTMA has better convergence with better solution diversity in comparison to the other competing algorithms. A comprehensive analysis based on statistical results reveal that the proposed FTMA is superior for obtaining better non-dominating pareto-fronts as compared to other competing methods.

Constraint subsets-based evolutionary multitasking for constrained multiobjective optimization

Constrained multiobjective optimization problems (CMOPs) are challenging because they need to optimize multiple conflicting objectives and satisfy various constraints simultaneously. To solve CMOPs, various constrained multiobjective evolutionary algorithms have been proposed in recent years. However, most of them tackle constraints by considering all constraints or zero constraint scenarios, these extreme treatment methods may be unable to utilize the information of multiple constraint subsets composed of partial constraints to maintain more promising infeasible solutions. To remedy this issue, a constraint subsets-based evolutionary multitasking method is developed, where a CMOP is transformed into a multitasking optimization problem by creating multiple simple auxiliary CMOPs. Particularly, the original CMOP is the main task, while the newly created CMOPs with different constraint subsets are the auxiliary tasks. Each task will be evolved by one specific population, and the knowledge transfer is conducted among tasks to realize the collaborative search. Meanwhile, the updating of the auxiliary task involves two stages. In the first stage, each auxiliary population will evolve to approach the pareto front of the constraint subset (s-CPF), promoting crossing the infeasible regions. While in the second stage, a feasible region-based search mechanism is proposed to approach the pareto front of the main task from each s-CPF, by utilizing unique and promising infeasible solutions. In addition, systematic experiments are carried out on three benchmark test suites and four real-world CMOPs. The experimental results fully demonstrate that the proposed algorithm is highly competitive with other state-of-the-art constrained multiobjective evolutionary algorithms.

A double auxiliary optimization constrained multi-objective evolutionary algorithm

In evolutionary constrained multi-objective optimization, the use of auxiliary optimization is gradually attracting attention. It is noted that different forms of auxiliary optimization have different advantages. Combining these advantages in an appropriate manner can further improve the algorithm’s performance. Motivated by this inspiration, we propose a double auxiliary optimization constrained multi-objective evolutionary algorithm, namely DAO. In DAO, two auxiliary optimizations, i.e., the unconstrained optimization and the (M+1)-objective optimization, are applied in a proposed tri-population co-evolution strategy. In this strategy, three populations are used to optimize the core optimization and the two auxiliary optimizations in an interactive evolution form. Furthermore, DAO develops a (M+1)-objective environmental selection strategy to deeply explore the boundary between the feasible and infeasible regions. In experimental studies, the performance of DAO and four other state-of-the-art algorithms is evaluated on three distinct benchmark test suites and two intricate real-world problem scenarios. The outcome of the comprehensive evaluation shows the competitive of DAO solving constrained multi-objective optimization problems.

Generating Cheap Representative Functions for Expensive Automotive Crashworthiness Optimization

Solving real-world engineering optimization problems, such as automotive crashworthiness optimization, is extremely challenging, because the problem characteristics are oftentimes not well understood. Furthermore, typical hyperparameter optimization approaches that require a large function evaluation budget are computationally hindered, if the function evaluation is expensive, e.g., requires finite element (FE) simulation runs. In this paper, we propose an approach to characterize real-world expensive black-box optimization problems using the exploratory landscape analysis (ELA). Based on these landscape characteristics, we can identify test functions that are fast-to-evaluate and representative for hyperparameter optimization purposes. Focusing on 20 problem instances from automotive crashworthiness optimization, our results reveal that these 20 crashworthiness problems exhibit landscape features different from classical optimization benchmark test suites, such as the widely-used black-box optimization benchmarking (BBOB) problem set. In fact, these 20 problem instances belong to problem classes that are distinct from the BBOB test functions based on the clustering results. Further analysis indicates that, as far as the ELA features concern, they are most similar to problem classes of tree-based test functions. By analyzing the performance of two optimization algorithms with different hyperparameters, namely the covariance matrix adaptation evolutionary strategy (CMA-ES) and Bayesian optimization (BO), we show that the tree-based test functions are indeed representative in terms of predicting the algorithm performances. Following this, such scalable and fast-to-evaluate tree-based test functions have promising potential for automated design of an optimization algorithm for specific real-world problem classes.

Decision variable classification based multi-objective multifactorial memetic algorithm for multi-objective multi-task optimization problem

Multi-task multi-objective optimization problems need to consider the algorithm's convergence and the population's diversity. The information transfer of decision variables with different characteristics may harm the effect of knowledge reuse. This paper proposes a novel hybrid multi-objective multifactorial memetic algorithm to address this issue. The proposed variable classification method will classify decision variables into convergence-related and diversity-related decision variables. Only the same type of decision variables in the source and target tasks can transfer information to avoid negative transfer. Different evolutionary operators are adopted according to the characteristics of decision variables during individual recombination. In addition, the proposed algorithm hybridizes the immune algorithm as the global evolutionary operator and the evolutionary gradient search algorithm as the local search operator into the multifactorial framework to enhance the searching ability. Finally, the proposed algorithm is compared with the state-of-the-art multi-objective evolutionary multitasking algorithms. The results of the experiments show that the proposed algorithm can achieve promising performance on the classical and complex multi-task multi-objective benchmark test suites.

Assessing effectiveness of test suites: what do we know and what should we do?

Background. Software testing is a critical activity for ensuring the quality and reliability of software systems. To evaluate the effectiveness of different test suites, researchers have developed a variety of metrics. Problem. However, comparing these metrics is challenging due to the lack of a standardized evaluation framework including comprehensive factors. As a result, researchers often focus on single factors (e.g., size), which finally leads to different or even contradictory conclusions. After comparing dozens of pieces of work in detail, we have found two main problems most troubling to our community: (1) researchers tend to oversimplify the description of the ground truth they use, and (2) data involving real defects is not suitable for analysis using traditional statistical indicators. Objective. We aim at scrutinizing the whole process of comparing test suites for our community. Method. To hit this aim, we propose a framework ASSENT (ev A luating te S t S uite E ffective N ess me T rics) to guide the follow-up research for evaluating a test suite effectiveness metric. ASSENT consists of three fundamental components: ground truth, benchmark test suites, and agreement indicator. Its functioning is as follows: first, users clarify the ground truth for determining the real order in effectiveness among test suites. Second, users generate a set of benchmark test suites and derive their ground truth order in effectiveness. Third, users use the metric to derive the order in effectiveness for the same test suites. Finally, users calculate the agreement indicator between the two orders derived by two metrics. Result. With ASSENT, we are able to compare the accuracy of different test suite effectiveness metrics. We apply ASSENT to evaluate representative test suite effectiveness metrics, including mutation score and code coverage metrics. Our results show that, based on the real faults, mutation score and subsuming mutation score are the best metrics to quantify test suite effectiveness. Meanwhile, by using mutants instead of real faults, test effectiveness will be overestimated by more than 20% in values. Conclusion. We recommend that the standardized evaluation framework ASSENT should be used for evaluating and comparing test effectiveness metrics in the future work.

Constrained evolutionary optimization based on dynamic knowledge transfer

While utilizing evolutionary algorithms (EAs) to solve constrained optimization problems (COPs), seeking feasible solutions that satisfy the constraints is the primary concern. In some cases, utilizing the knowledge of the objective may facilitate the exploration of feasible regions and the exploitation of global optima. To strike a balance between objective and constraints, we attempt to address two problems, namely whether transferring the knowledge of objective in the early evolutionary stage and how to dynamically transfer the knowledge of objective to constraints. First, whether transferring in the early stage is determined by the characteristics of the objective, which has been classified to simple and complex based on the variation of population distribution. Second, how to transfer is solved by designing suitable constraint handling techniques (CHTs). To deal with COPs with simple objective, an objective-oriented CHT is proposed, where an indicator called Knowledge Transfer Rate (KTR) representing the relationship between the objective and constraints is mined and used. For COPs with complex objective, a constraint-oriented CHT is proposed, which includes a constraint-driven strategy and a hybrid-driven strategy. The proposed method is executed on three benchmark test suites, namely IEEE CEC2006, CEC2010, and CEC2017, it achieves superior or at least competitive performance in comparison with other state-of-the-art methods. Furthermore, the proposed method is successfully implemented in a redundant robotic manipulator motion planning problem.

A stable-state multi-objective evolutionary algorithm based on decomposition

The decomposition-based multi-objective evolutionary algorithm (MOEA/D) has been shown to effectively solve real-world multi-objective optimization problems (MOPs). The uniformly distributed weight vectors guide the population to continuously evolve towards the Pareto front (PF). However, the random matching mode between individuals and weight vectors leads to some excellent individuals being replaced in the evolution process. Meanwhile, the number of replaced individuals in the neighborhood is not controlled. This causes a waste of computing resources and fails to balance the diversity and convergence of the population effectively. Referring to the influence on body adjustment of homeostasis in the medical field, the stable-state mechanism is proposed to keep the dynamic balance between exploration and exploitation. Therefore, this paper presents the stable-state multi-objective evolutionary algorithm based on decomposition (MOEA/D-SS) that adopts a new stable-state replacement strategy to adjust the number of replaced individuals within each neighborhood. Furthermore, a stable-state neighborhoods adjustment strategy was proposed to adjust the size of each neighborhood. This mechanism can adjust the convergence and diversity of newly generated individuals at different stages. Finally, several benchmark test suites (i.e., ZDT, DTLZ, and UF) and a practical optimization problem are used to test the performance of MOEA/D-SS. The experimental results demonstrate that the proposed algorithm outperforms other comparative algorithms.

RenderBench: The CPU Rendering Benchmark Suite Based on Microarchitecture-Independent Characteristics

This research addresses the issue of evaluating CPU rendering performance by introducing the innovative benchmark test suite construction method RenderBench. This method combines CPU microarchitecture features with rendering task characteristics to comprehensively assess CPU performance across various rendering tasks. Adhering to principles of representativeness and comprehensiveness, the constructed benchmark test suite encompasses diverse rendering tasks and scenarios, ensuring accurate capture of CPU performance features. Through data sampling and in-depth analysis, this study focuses on the role of microarchitecture-independent features in rendering programs, including instruction-level parallelism, instruction mix, branch prediction capability, register dependency distance, data flow stride, and memory reuse distance. The research findings reveal significant variations in rendering programs across these features. For instance, in terms of instruction-level parallelism, rendering programs demonstrate a high level of ILP (instruction-level parallelism), with an average value of 5.70 for ILP256, surpassing benchmarks such as Mibench and NAS Parallel Benchmark. Furthermore, in aspects such as instruction mix, branch prediction capability, register dependency distance, data flow stride, and memory reuse distance, rendering programs exhibit distinct characteristics. Through the application of the RenderBench method, a scalable and highly representative benchmark test suite was constructed, facilitating an in-depth exploration of CPU performance bottlenecks in rendering tasks. By delving into microarchitecture-independent features, this study provides profound insights into rendering program performance, offering valuable guidance for optimizing CPU rendering performance. The application of ensemble learning models, such as random forest, XGBoost, and ExtraTrees, reveals the significant influence of features like floating-point computation, memory access patterns, and register usage on CPU rendering program performance. These insights not only offer robust guidance for performance optimization but also underscore the importance of feature selection and algorithm choice. In summary, the results of feature importance ranking in this study provide beneficial directions and deep insights for the optimization and enhancement of CPU rendering program performance. These findings are poised to exert a positive impact on future research and development endeavors.

Memory guided Aquila optimization algorithm with controlled search mechanism for seismicity analysis of earthquake prone regions

De-clustering the seismic catalog is one of the crucial processes in determining the probability of exceeding ground motions at particular locations. Removing dependent events, such as foreshocks and aftershocks generated from mainshocks, from an earthquake catalog is known as seismicity de-clustering. This paper presents a new approach to classify seismicity using a swarm intelligence technique called the memory-guided Aquila optimizer (MGAOA). Aquila optimization is a recently reported yet popular optimization algorithm inspired by the hunting process of Aquila. In MGAOA, the Aquila searches for the prey based on the personal best history stored in the memory element, which helps MGAOA to converge faster and maintain the balance between exploration and exploitation during local and global search procedures. The search-controlled parameter is further integrated with Aquila optimization to enhance the exploitation phase of the algorithm. The effectiveness of the proposed MGAO is tested on twenty-three classical test functions and ten benchmark test suites of IEEE CEC 2021. The results show that the proposed MGAOA outperforms other algorithms regarding performance metrics and statistical tests. The MGAOA is applied to solve the problem of seismicity de-clustering using the nearest neighbor distance (NND). The NND is a widely used parameter for seismicity and consists of space–time-magnitude information. The MGAOA-based de-clustering model is used to identify the seismic clusters in highly active earthquake-prone Himalayas, California, Japan, and Indonesia regions. The results obtained by the proposed model are compared with state-of-the-art de-clustering techniques and other memory-guided swarm intelligence techniques. The obtained results in terms of aftershocks and backgrounds are evaluated with the help of cumulative plot, λ-plot, space–time plot, inter-event time versus inter-event distance plot, and statistical parameters like coefficient of variance, which show that the proposed model efficiently detects the seismic clusters, and outperforms other benchmark de-clustering techniques.