Population-based Metaheuristic Algorithm Research Articles

Parameters optimization of PEMFC model based on gazelle optimization algorithm

Fuel cells (FCs) play a crucial role in converting stored hydrogen energy into electricity. However, accurately modeling and optimizing their performance is challenging due to the lack of critical parameter data—specifically, seven key parameters including four semi-empirical coefficients (ξ1, ξ2, ξ3, ξ4), an adjusting parametric coefficient (β), a constant equivalent resistance (Rc), and an adjustable parameter (λ). These essential parameters are typically not provided in manufacturers' datasheets, creating a significant gap in the precise calibration and optimization of Proton Exchange Membrane Fuel Cells (PEMFCs).This study addresses this gap by applying seven population-based meta-heuristic algorithms to estimate and optimize these unknown parameters. Among these, the Gazelle Optimization Algorithm (GOA) is identified as particularly effective, offering superior precision and rapid convergence. Our research evaluates the performance of these algorithms using indicators such as Standard Deviation (StD) and Sum of Squared Errors (SSE). The GOA achieved exceptionally low SSE values of 7.637606 × 10^-3, 1.28694222 × 10−2, and 2.288128 for the Horizon 500W, BCS 500W, and NedStack PS6 stacks, respectively, along with corresponding StD values of 2.275703 × 10−9, 9.12077649 × 10−15, and 3.26518838 × 10−14. These results underscore the algorithm's accuracy and effectiveness in optimizing PEMFC parameters, closely aligning with the manufacturers' polarization curves.The study's findings, validated across these three different fuel cell stacks, highlight the GOA's superiority over other methods in terms of accuracy and convergence speed. This manuscript contributes to the field by providing a robust method for accurately optimizing PEMFC parameters, which are critical for enhancing the overall performance of fuel cells. The results also demonstrate the GOA's potential as a superior optimization tool in the field of fuel cell technology.

Comparative Study of Population-based Metaheuristic Algorithms in Case Study of DNA Sequence Assembly

Comparative Study of Population-based Metaheuristic Algorithms in Case Study of DNA Sequence Assembly

Techniques for Improving Genetic Algorithms in Solving Operating Room Scheduling Problems: An Integrative Review

Operating room scheduling is a complex process that involves various resources and takes the interests of many parties into consideration. The genetic algorithm is the frequently used metaheuristic algorithm to solve a large-size operating room scheduling problem. Many techniques have been developed to improve the genetic algorithms' performance in dealing with the operating room scheduling complexity. In this paper, we survey available literature to identify improvement techniques used at each stage of the genetic algorithm and capture the underlying problems. This review provides a mapping of improvement techniques in genetic algorithms correlating with the considered problems. The results can be employed by other researchers as a guide for future research in integrating a genetic algorithm or other population-based metaheuristic algorithm with a recent heuristic algorithm following the future directions of operating room scheduling research.

Metaheuristic-based energy-aware image compression for mobile app development

AbstractThe widely applied JPEG standard has undergone recent efforts using population-based metaheuristic (PBMH) algorithms to optimise quantisation tables (QTs) for specific images. However, user preferences, like an Android developer’s preference for small-size images, are often overlooked, leading to high-quality images with large file sizes. Another limitation is the lack of comprehensive coverage in current QTs, failing to accommodate all possible combinations of file size and quality. Therefore, this paper aims to propose three distinct contributions. First, to include the user’s opinion in the compression process, the file size of the output image can be controlled by a user in advance. To this end, we propose a novel objective function for population-based JPEG image compression. Second, we suggest a novel representation to tackle the lack of comprehensive coverage. Our proposed representation can not only provide more comprehensive coverage but also find the proper value for the quality factor for a specific image without any background knowledge. Both representation and objective function changes are independent of the search strategies and can be used with any population-based metaheuristic (PBMH) algorithm. Therefore, as the third contribution, we also provide a comprehensive benchmark on 22 state-of-the-art and recently-introduced PBMH algorithms on our new formulation of JPEG image compression. Our extensive experiments on different benchmark images and in terms of different criteria show that our novel formulation for JPEG image compression can work effectively.

Secretary bird optimization algorithm: a new metaheuristic for solving global optimization problems

This study introduces a novel population-based metaheuristic algorithm called secretary bird optimization algorithm (SBOA), inspired by the survival behavior of secretary birds in their natural environment. Survival for secretary birds involves continuous hunting for prey and evading pursuit from predators. This information is crucial for proposing a new metaheuristic algorithm that utilizes the survival abilities of secretary birds to address real-world optimization problems. The algorithm's exploration phase simulates secretary birds hunting snakes, while the exploitation phase models their escape from predators. During this phase, secretary birds observe the environment and choose the most suitable way to reach a secure refuge. These two phases are iteratively repeated, subject to termination criteria, to find the optimal solution to the optimization problem. To validate the performance of SBOA, experiments were conducted to assess convergence speed, convergence behavior, and other relevant aspects. Furthermore, we compared SBOA with 15 advanced algorithms using the CEC-2017 and CEC-2022 benchmark suites. All test results consistently demonstrated the outstanding performance of SBOA in terms of solution quality, convergence speed, and stability. Lastly, SBOA was employed to tackle 12 constrained engineering design problems and perform three-dimensional path planning for Unmanned Aerial Vehicles. The results demonstrate that, compared to contrasted optimizers, the proposed SBOA can find better solutions at a faster pace, showcasing its significant potential in addressing real-world optimization problems.

An efficient improved exponential distribution optimizer: application to the global, engineering and combinatorial optimization problems

Population-based meta-heuristic optimization algorithms play a vital role in addressing optimization problems. Nowadays, exponential distribution optimizer (EDO) can be considered to be one of the most recent among these algorithms. Although it has achieved many promising results, it has a set of shortcomings, for example, the decelerated convergence, and provides local optima solution as it cannot escape from local regions in addition to imbalance between diversification and intensification. Therefore, in this study, an enhanced variant of EDO called mEDO was proposed to address these shortcomings by combining two efficient search mechanisms named orthogonal learning (OL) and local escaping operator (LEO). In mEDO, the LEO has been exploited to escape local optima and improve the convergence behavior of the EDO by employing random operators to maximize the search process and to effectively discover the globally optima solution. Then the OL has been combined to keep the two phases (i.e., exploration and exploitation) balanced. To validate the effectiveness and performance of the mEDO algorithm, the proposed method has been evaluated over ten functions of the IEEE CEC’2020 test suite as well as eight real-world applications (engineering design optimization problems), Furthermore we test the applicability of the proposed algorithm by tackling 21 instance of the quadratic assignment problem (QAP). The experimental and statistical results of the proposed algorithm have been compared against seven other common metaheuristic algorithms (MAs), including the basic EDO. The results show the supremacy of the mEDO algorithm over the other algorithms and reveal the applicability and effectiveness of the mEDO algorithm compared to well-established metaheuristic algorithms. The experimental results and different statistical measures revealed the reliability and applicability of the mEDO method in solving the global, engineering design, and combinatorial optimization problems by achieving a reasonable solution in terms of scoring a global optima solutions and avoiding premature convergence by increasing the population’s diversity.

Dynamic metaheuristic selection via Thompson Sampling for online optimization

It is acknowledged that no single heuristic can outperform all the others in every optimization problem. This has given rise to hyper-heuristic methods for providing solutions to a wider range of problems. In this work, a set of five non-competing low-level heuristics is proposed in a hyper-heuristic framework. The multi-armed bandit problem analogy is efficiently leveraged and Thompson Sampling is used to actively select the best heuristic for online optimization. The proposed method is compared against ten population-based metaheuristic algorithms on the well-known CEC’05 optimizing benchmark consisting of 23 functions of various landscapes. The results show that the proposed algorithm is the only one able to find the global minimum of all functions with remarkable consistency.

Design of Runge-Kutta optimization for fractional input nonlinear autoregressive exogenous system identification with key-term separation

Population-based metaheuristic algorithms have gained significant attention in research community due to its effectiveness in solving complex optimization problems in diverse fields. In this study, knacks of population-based Runge-Kutta optimizer (RUN) are exploited for the identification of fractional input non-linear exogenous auto-regressive (FINARX) system with key term separation. The fractional order calculus operator of the Grünwald-Letnikov derivative is exploited to develop FINARX from a conventional non-linear auto-regressive exogenous system. The identification scheme for FINARX model is implemented through a mean-square-error-based fitness function. RUN utilizes the slope variations calculated by the well-known Runge-Kutta method for an effective search mechanism in the exploration and exploitation phases. Moreover, an enhanced solution quality mechanism is employed for speedy convergence and keeping the movement toward the best solution by escaping the local optima. The robustness of the algorithm is analyzed by multiple variations of non-linearity as well as different noise scenarios. The performance of the RUN to identify the FINARX system is validated in terms of convergence rate, fitness value, robustness, and accuracy in weight estimation. The effectiveness of the RUN is further assessed through exhaustive simulations with their statistics as well as comparison with the standard recent counterparts, including the Whale optimization algorithm, Reptile Search algorithm, and Aquila optimizer on different performance indices for the FINARX system.

A Population Initialization Method Based on Similarity and Mutual Information in Evolutionary Algorithm for Bi-objective Feature Selection

Feature selection (FS) is an important data pre-processing technique in classification. It aims to remove redundant and irrelevant features from the data, which reduces the dimensionality of data and improves the performance of the classifier. Thus, FS is a bi-objective optimization problem, and evolutionary algorithms (EAs) have been proven to be effective in solving bi-objective FS problems. EA is a population-based metaheuristic algorithm, and the quality of the initial population is an important factor affecting the performance of EA. An improper initial population may negatively affect the convergence speed of the EA and even make the algorithm fall into the local optimum. In this paper, we propose a similarity and mutual information-based initialization method, named SMII, to improve the quality of the initial population. This method determines the distribution of initial solutions based on similarity, and shields features with high correlation to the selected features according to mutual information. In the experiment, we embed SMII, the latest four initialization methods, and a traditional random initialization method into NSGA-II, and compared their performance on 15 public datasets. The experimental results show that SMII performs best on most datasets, and can effectively improve the performance of the algorithm. Moreover, we compare the performance of two other EA before and after embedding SMII on 15 datasets, the results further prove that the proposed method can effectively improve the search capability of the EA for FS.

Dynamic routing optimization in software-defined networking based on a metaheuristic algorithm

Optimizing resource allocation and routing to satisfy service needs is paramount in large-scale networks. Software-defined networking (SDN) is a new network paradigm that decouples forwarding and control, enabling dynamic management and configuration through programming, which provides the possibility for deploying intelligent control algorithms (such as deep reinforcement learning algorithms) to solve network routing optimization problems in the network. Although these intelligent-based network routing optimization schemes can capture network state characteristics, they are prone to falling into local optima, resulting in poor convergence performance. In order to address this issue, this paper proposes an African Vulture Routing Optimization (AVRO) algorithm for achieving SDN routing optimization. AVRO is based on the African Vulture Optimization Algorithm (AVOA), a population-based metaheuristic intelligent optimization algorithm with global optimization ability and fast convergence speed advantages. First, we improve the population initialization method of the AVOA algorithm according to the characteristics of the network routing problem to enhance the algorithm’s perception capability towards network topology. Subsequently, we add an optimization phase to strengthen the development of the AVOA algorithm and achieve stable convergence effects. Finally, we model the network environment, define the network optimization objective, and perform comparative experiments with the baseline algorithms. The experimental results demonstrate that the routing algorithm has better network awareness, with a performance improvement of 16.9% compared to deep reinforcement learning algorithms and 71.8% compared to traditional routing schemes.

Optimal Allocation of Distributed Generations and Capacitor Banks in Distribution Systems Using Arithmetic Optimization Algorithm

In this paper, an optimization approach based on an arithmetic optimization algorithm (AOA) is proposed for specifying the optimal allocation of distribution generations/generators (DGs) and capacitor banks (CBs) in radial distribution systems. The AOA is a new population-based meta-heuristic algorithm that is essentially based on using basic arithmetic operators in mathematics. The proposed approach is employed to specify the optimum placement, capacity, and power factor of DGs and CBs to decrease the distribution systems’ total power loss and voltage deviation. To state the performance of the proposed approach, DGs and CBs are placed in IEEE 33-bus and 69-bus systems separately or together. When only DGs are used and the parameters of location, capacity, and power factor of DGs are determined simultaneously, the total active power loss reductions in the IEEE 33-bus and 69-bus systems are achieved at 94.42% and 98.03%, respectively. When the results of other optimization algorithms are examined, it is seen that better results are obtained with AOA.

Optimization of hybrid platform for high-tech equipment and building vibration mitigation using evolutionary algorithms

In this paper, an ideal fuzzy logic control (FLC) algorithm is designed for micro vibration control of a hybrid platform which is placed on a second floor of the building subjected to traffic-induced ground vibrations. A hybrid platform is one that can be utilized to safeguard high precision equipment from vibrations caused by traffic, machinery, and natural hazards like earthquakes while it is situated with in the building structure. A fuzzy system is employed to obtain the appropriate control force of active actuators, and the fuzzy system is then optimized using four evolutionary algorithms (EAs), which are population-based metaheuristic algorithms that were inspired by the nature. A microgenetic algorithm (µ-GA), particle swarm optimization (PSO), differential evolutionary algorithm (DEA), and cuckoo search algorithm (CSA) are used to optimize the FLC knowledge base and rule base. To illustrate the capabilities of the proposed method in traffic-induced vibration control of a hybrid platform, a three-degree-of freedom (3-DOF) structure along with the platform is taken into account. Finally, utilizing the Bolt, Beranek and Newman (BBN)-vibration criteria and the absolute velocity of hybrid platform as the objective, the efficiency of the hybrid platform microvibration control is evaluated. The simulation results obtained from these novel EAs indicated that the actively controlled platform is efficacious in reducing microvibration of high-tech equipment. Based on the results, the PSO algorithm optimized fuzzy controller outperforms the other methods in terms of velocity levels of second floor of the building and hybrid platform.

Simple population-based algorithms for solving optimization problems

Heuristic algorithms are simple yet powerful tools that are capable of yielding acceptable results in a reasonable execution time. Hence, they are being extensively used for solving optimization problems by researchers nowadays. Due to the quantum of computing power and hardware available today, a large number of dimensions and objectives are considered and analyzed effectively. This paper proposes new population-based metaheuristic algorithms that are capable of combining different strategies. The new strategies help in fast converging as well as trying to avoid local optima. The proposed algorithms could be used as single-phase as well as two-phase algorithms with different combinations and tuning parameters. “Best”, “Mean” and “Standard Deviation” are computed for thirty trials in each case. The results are compared with many efficient optimization algorithms available in the literature. Sixty-one popular un-constrained benchmark problems with dimensions varying from two to thousand and fifteen constrained real-world engineering problems are used for the analyses. The results show that the new algorithms perform better for several test cases. The suitability of the new algorithms for solving multi-objective optimization problems is also studied using five numbers of two-objective ZDT problems. Pure Diversity, Spacing, Spread and Hypervolume are the metrics used for the evaluation.

New modified Bat algorithm for blind speech enhancement in time domain

We address the speech enhancement problem for dual convolutif mixed channel by viewing it in a blind separation sources setting. One widely used technique to separate mixed signals is to apply adaptive filtering, the challenge is to identify an unknown finite impulse response. traditionally we apply a gradient-based algorithm to adapt filter coefficients. However, such algorithm often suffers from premature convergence ,when using large filters and non-stationary inputs , leading to the so-called local minimum problem , which affects the quality of enhanced signals significatively .one alternative to overcome this problem is to apply a population-based metaheuristic algorithms in which filter coefficients are adapted iteratively by minimizing a cost function .But even with this metaheuristic based solution, local minimum problem at large filters still exists. In order to avoid local minima and improve the chance to reach the global solution, we propose in this paper, a novel algorithm called a modified bat algorithm to render the search process efficiently by enhancing its capability of exploration and exploitation. Several experiments under different noise types are carried out using our proposed modified bat algorithm in comparison with some of the popular state-of-the-art algorithms. The enhanced signals obtained by each algorithm at the separation process outputs, show good behavior and superiority of our proposed algorithm. In terms of system misalignment, as well as a segmental signal-to-noise ratio.

A hybrid meta-heuristic algorithm for multi-objective IoT service placement in fog computing environments

The fog computing paradigm is promising for deploying various delay-sensitive Internet of Things (IoT) applications. The resource-constrained fog devices restrict the number of application deployments due to a lack of efficient resource estimation and discovery mechanisms for various emergent heterogeneous IoT applications. An efficient resource allocation strategy is one of the best choices to meet these application’s Quality of Service (QoS) requirements and improve system performance. However, finding the best allocation strategy for IoT applications with more than one QoS parameter is a challenge, and it has been proved as a non-deterministic polynomial time (NP)-complete problem. This article formulates a classical weighted multi-objective IoT service placement to optimize three parameters, i.e., makespan, cost, and energy. The non-convexity nature of the solution space motivates us to focus on the population-based meta-heuristic algorithm, i.e. Genetic Algorithm (GA), Simulated Annealing (SA) and Particle Swarm Optimization (PSO), along with their combination GA-SA, and GA-PSO. It implements the algorithm and compares it with the greedy-based random placement approach, varying the number of IoT applications with different parameters. The final results reveal that the hybrid method GA-SA outperforms other state-of-the-art algorithms.

Newton-Raphson-based optimizer: A new population-based metaheuristic algorithm for continuous optimization problems

The Newton-Raphson-Based Optimizer (NRBO), a new metaheuristic algorithm, is suggested and developed in this paper. The NRBO is inspired by Newton-Raphson's approach, and it explores the entire search process using two rules: the Newton-Raphson Search Rule (NRSR) and the Trap Avoidance Operator (TAO) and a few groups of matrices to explore the best results further. The NRSR uses a Newton-Raphson method to improve the exploration ability of NRBO and increase the convergence rate to reach improved search space positions. The TAO helps the NRBO to avoid the local optima trap. The performance of NRBO was assessed using 64 benchmark numerical functions, including 23 standard benchmarks, 29 CEC2017 constrained benchmarks, and 12 CEC2022 benchmarks. In addition, the NRBO was employed to optimize 12 CEC2020 real-world constrained engineering optimization problems. The proposed NRBO was compared to seven state-of-the-art optimization algorithms, and the findings showed that the NRBO produced promising results due to its features, such as high exploration and exploitation balance, high convergence rate, and effective avoidance of local optima capabilities. In addition, the NRBO also validated on challenging wireless communication problem called the internet of vehicle problem, and the NRBO was able to find the optimal path for data transmission. Also, the performance of NRBO in training the deep reinforcement learning agents is also studied by considering the mountain car problem. The obtained results also proved the NRBO's excellent performance in handling challenging real-world engineering problems.

Optimization of reinforced concrete structures using population-based metaheuristic algorithms

For many industrial activities, ideal projects are achieved by comparing the solution of alternative projects with those already executed. The feasibility of solutions plays an important role in these activities. For example, the underlying objective (cost, profit, etc.) estimated for each project solution is calculated and the best solution is adopted. This is the usual procedure followed by many constructors due to time and resource limitations. However, in many cases, this method is followed simply by a lack of knowledge of existing optimization procedures. In this context, a comparative study of population-based metaheuristic algorithms applied to a case study of a reinforced concrete beam design reinforced with a polymer matrix with carbon fibers will be presented. Evolutionary algorithms have the ability to determine the optimal values of the design variables without disregarding the restrictions on ACI-318 and ACI-440 standards while minimizing the reinforcement area for each beam (cost). The comparative study shows that not all presented algorithms violated design constraints. In addition, it can be said that the values found for the design variables present a low dispersion around the mean value of the objective function.

Grey Wolf Optimization Algorithm: A Survey

The Gray Wolf Optimizer (GWO) is a population-based meta-heuristic algorithm that belongs to the family of swarm intelligence algorithms inspired by the social behavior of gray wolves, in particular the social hierarchy and hunting mechanism. Because of its simplicity, flexibility, and few parameters to be tuned, it has been applied to a wide range of optimization problems. And yet it has some disadvantages, such as poor exploration skills, stagnation at local optima, and slow convergence speed. Therefore, different variants of GWO have been proposed and developed to address these disadvantages. In this article, some literature, especially from the last five years, has been reviewed and summarized by well-known publishers. First, the inspiration and the mathematical model of GWO were explained. Subsequently, the improved GWO variants were divided into four categories and discussed. After that, each variant's methodology and experiments were explained and clarified. The study ends with a summary conclusion of the main foundation of GWO and suggests some possible future directions that can be explored further.

EWSO: Boosting White Shark Optimizer for solving engineering design and combinatorial problems

Population-based meta-heuristic algorithms are crucial for solving optimization issues. One of these recent algorithms that is now believed to be promising metaheuristic algorithm is the White Shark Optimizer (WSO). Although it has produced a number of encouraging results, it has some certain downsides like other metaheuristic algorithms (MAs). Dropping into the local minimum optima and local solution zones, the uneven distribution of exploration and exploitation abilities, and the slow rate of convergence are some of these downsides. To fight those, two efficient mechanisms, i.e., Enhanced Solution Quality (ESQ) and Orthogonal Learning (OL), have been applied to develop an enhanced version of WSO called EWSO. The effectiveness of EWSO has been comprehensively evaluated using the IEEE CEC’2022 test suite. For further verification and achieving the principle of generality, the proposed algorithm has been used to provide good solutions for three engineering design issues (i.e., Gear train, Vertical deflection of an I beam, and the piston lever), for further applicability it has also been employed to solve two combinatorial optimization problems (i.e., bin packing problem (BPP) and quadratic assignment problems (QAP)). This effectiveness has been evaluated compared to the most recent and common metaheuristics, i.e., Kepler Optimization Algorithm (KOA), Seagull Optimization Algorithm (SOA), Spider Wasp Optimizer (SWO), and some well-known metaheuristic algorithms such as; Sine cosine Algorithm (SCA), Whale Optimization Algorithm (WOA), and Trees Social Relations Optimization (TSR), in addition to the original SWO. The experimental results and statistical measures confirm the effectiveness and reliability of the proposed algorithm (EWSO) in tackling real-world issues. It is able to overcome the previous drawbacks by providing the global optimum and preventing premature convergence through an increase in population diversity.

Optimization of Truss Structures by Using a Hybrid Population-Based Metaheuristic Algorithm

Optimization of Truss Structures by Using a Hybrid Population-Based Metaheuristic Algorithm