Efficient Metaheuristic Algorithm Research Articles

An Improved Driving Training-Based Optimization Algorithm for the Minimum Gap Graph Connected Partition Problem

In this paper, we consider the minimum gap partition problem in an undirected connected graph with nonnegative vertex weights. This problem involves in dividing the given graph into a specified number of connected subgraphs such that the total difference between the largest and the smallest vertex weights in each subgraph is minimized. Namely, let G = (V, E, W) be a given undirected connected graph with vertex set V , edge set E, and nonnegative vertex weight function W : V → R+, and let k ≥ 2 be a given positive integer. The minimum gap partition problem consists of constructing a partition P = {V1, V2, . . . , Vk} of non-empty and pairwise disjoint subsets of V such that each vertex set Vi, i = 1, 2, . . . , k, induces a connected subgraph G[Vi] of G. The objective of the problem is to find such a partition of V that minimizes the total difference 1≤i≤k maxv∈Viw(v) − minv∈Viw(v). This problem, as many other variants of graph partition problems, is known to be NP-hard problem. We designed an efficient metaheuristic algorithm for finding approximate solution of large-scale instances of this problem. The quality of the proposed algorithm is assessed comparing with the previous algorithms proposed for the problem.

Virtual synchronous generator control with parameters optimization based on new WaOA algorithm for grid-feeding inverters

The widespread integration of renewable energy sources (RESs) within traditional power systems causes a considerable impact, such as a decrease in total inertia, damping properties, and large frequency deviation. To tackle this issue, numerous research have been proposed to introduce the virtual synchronous generator (VSG) control strategy as an effective solution applied to power electronics inverters operating in grid-forming or grid-feeding control modes. However, the proper tunning of the inertia and damping parameters is considered as a significant challenge to ensure the system stability and reliability. In this paper, an efficient meta-heuristic Walrus Optimization Algorithm (WaOA) -based VSG control intended for grid-feeding inverters within a weak grid is proposed. The objective of this work is to offer a proper tuning of the VSG parameters, thereby improving the dynamic characteristics of the microgrid (MG) and guaranteeing a stable operation. In addition, it highlights the adopted optimization technique which is introduced on VSG control while ensuring minimum objective function value. Through offline simulations in MATLAB/Simulink, the superiority of the suggested WaOA method is first confirmed, in which it offers the lowest Integral Square Error (ISE) fitness function value of 0.026, compared to the Mountain Gazelle Optimizer (MGO) and Tunicate Swarm Algorithm (TSA) with 0.027 9 and 0.0281, respectively. Second, the performance of the WaOA-based VSG control is compared to the ones based on MGO and TSA techniques under active and reactive power variations. The obtained results demonstrate that the proposed VSG control ensures a better transient performance, especially in terms of under and overshoots.

Efficient neural network-aided seismic life-cycle cost optimization of steel moment frames

In this paper, a novel and efficient neural network-based methodology is proposed to achieve seismic total cost optimization of steel moment-resisting frames in a timely manner. The computational burden of an optimization process based on performing nonlinear time-history analysis is prohibitively high. To address this crucial issue, a new and efficient neural network model is proposed in this paper to accurately predict the nonlinear time history response of steel frames during the optimization process. In the proposed neural network model, an ensemble of parallel neural networks is used to provide excellent prediction accuracy. In addition, a new repairability constraint is proposed to check the seismic damage level of structures during the optimization process with the aid of the proposed neural network model. Moreover, an efficient metaheuristic algorithm is used to achieve the optimization task. Two numerical examples are illustrated to demonstrate the efficiency of the proposed methodology. The results show that the proposed neural network model outperforms the existing standard models in terms of prediction accuracy. Furthermore, it is shown that by using the proposed methodology, the optimal seismic total cost of steel frames increases by less than 2.5%, yet their seismic collapse capacity increases by at least 30%.

Learning cooking algorithm for solving global optimization problems

In recent years, many researchers have made a continuous effort to develop new and efficient meta-heuristic algorithms to address complex problems. Hence, in this study, a novel human-based meta-heuristic algorithm, namely, the learning cooking algorithm (LCA), is proposed that mimics the cooking learning activity of humans in order to solve challenging problems. The LCA strategy is primarily motivated by observing how mothers and children prepare food. The fundamental idea of the LCA strategy is mathematically designed in two phases: (i) children learn from their mothers and (ii) children and mothers learn from a chef. The performance of the proposed LCA algorithm is evaluated on 51 different benchmark functions (which includes the first 23 functions of the CEC 2005 benchmark functions) and the CEC 2019 benchmark functions compared with state-of-the-art meta-heuristic algorithms. The simulation results and statistical analysis such as the t-test, Wilcoxon rank-sum test, and Friedman test reveal that LCA may effectively address optimization problems by maintaining a proper balance between exploitation and exploration. Furthermore, the LCA algorithm has been employed to solve seven real-world engineering problems, such as the tension/compression spring design, pressure vessel design problem, welded beam design problem, speed reducer design problem, gear train design problem, three-bar truss design, and cantilever beam problem. The results demonstrate the LCA’s superiority and capability over other algorithms in solving complex optimization problems.

Artemisinin optimization based on malaria therapy: Algorithm and applications to medical image segmentation

This study proposes an efficient metaheuristic algorithm called the Artemisinin Optimization (AO) algorithm. This algorithm draws inspiration from the process of artemisinin medicine therapy for malaria, which involves the comprehensive eradication of malarial parasites within the human body. AO comprises three optimization stages: a comprehensive eliminations phase simulating global exploration, a local clearance phase for local exploitation, and a post-consolidation phase to enhance the algorithm's ability to escape local optima. In the experimental, this paper conducts a qualitative analysis experiment on the AO, explaining its characteristics in searching for the optimal solution. Subsequently, AO is then tested on the classical IEEE CEC 2014, and the latest IEEE CEC 2022 benchmark function sets to assess its adaptability. Comparative analyses are conducted against eight well-established algorithms and eight high-performance improved algorithms. Statistical analyses of convergence curves and qualitative metrics revealed AO's robust competitiveness. Lastly, the AO is incorporated into breast cancer pathology image segmentation applications. Using 15 authentic medical images at six threshold levels, AO's segmentation performance is compared against eight distinguished algorithms. Experimental results demonstrated AO's superiority in terms of image segmentation accuracy, Feature Similarity Index (FSIM), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM) over the contrast algorithms. These results emphasize AO's efficiency and its potential in real-world optimization applications. The source codes22https://github.com/aliasgharheidaricom/Artemisinin-Optimizer-using-Malaria-Therapy-Algorithm-and-Applications-to-Medical-Image-Segmentation. of this paper are available in https://aliasgharheidari.com/AO.html and other public websites33https://ch.mathworks.com/matlabcentral/fileexchange/165791-artemisinin-optimization-ao-based-on-malaria-therapy..

A Novel Symbol Detection Strategy based on Discrete Elephant Herding Optimization Algorithm for MIMO-FBMC/OQAM System

The rapidly growing technological enhancements in wireless communication have pushed the researchers to develop various transmission schemes in recent decades. Multiple-input multiple-output - filter bank multicarrier/offset quadrate amplitude modulation (MIMO-FBMC/OQAM) is one of these schemes with exclusive features. However, unless an efficient symbol detector is employed, we cannot fully benefit from the superior features of MIMO-FBMC/OQAM system as the symbol vectors cannot be detected accurately at the receiver side. An ideal symbol detector is expected to have the capability of recovering the symbol vectors in the most accurate way without causing too much enhancement in the computational complexity. To meet this expectation, we have modified the conventional maximum likelihood (ML) strategy. Instead of using an exhaustive search procedure, which guarantees the highest detection performance but causes an excessive computational load in the ML scheme, an efficient metaheuristic algorithm called discrete elephant herding optimization (DEHO) was employed for optimizing the symbol vectors. With the integration of DEHO to the conventional ML in the aforementioned way, we have not only obtained a considerable reduction in the complexity of ML, but also reached the near-optimal symbol detection performance by outperforming the other symbol detectors considered in this paper.

Hydrogen refueling station location optimization under uncertainty

Hydrogen refueling stations (HRS) are essential for the growth of the hydrogen energy and fuel cell vehicle industry, making the strategic decision of their location and scale critical. This study advances the HRS location optimization problem by integrating network planning and hydrogen scheduling optimization, a novel approach that addresses both where and how big HRSs should be, alongside managing supply relations and hydrogen transportation scheduling. Uniquely, it incorporates uncertainty factors in hydrogen demand, using a two-stage multi-period stochastic programming model designed to minimize both construction and operational costs under variable demand scenarios, while also ensuring customer satisfaction. Our main contributions include the development of this comprehensive model and the introduction of an efficient metaheuristic algorithm to solve it, significantly enhancing computational efficiency. The effectiveness of our approach is demonstrated through numerical experiments, and a sensitivity analysis provides crucial insights, underlining the practical implications for HRS infrastructure development. The findings not only offer a methodological advancement in HRS location selection but also deliver tangible results that can guide industry stakeholders in strategic decision-making, marking a significant step forward in the sustainable development of hydrogen energy infrastructure.

Genetic Engineering Algorithm (GEA): An Efficient Metaheuristic Algorithm for Solving Combinatorial Optimization Problems

Genetic Engineering Algorithm (GEA): An Efficient Metaheuristic Algorithm for Solving Combinatorial Optimization Problems

Genetic Engineering Algorithm (GEA): An Efficient Metaheuristic Algorithm for Solving Combinatorial Optimization Problems

Genetic Algorithms (GAs) are known for their efficiency in solving combinatorial optimization problems, thanks to their ability to explore diverse solution spaces, handle various representations, exploit parallelism, preserve good solutions, adapt to changing dynamics, handle combinatorial diversity, and provide heuristic search. However, limitations such as premature convergence, lack of problem-specific knowledge, and randomness of crossover and mutation operators make GAs generally inefficient in finding an optimal solution. To address these limitations, this paper proposes a new metaheuristic algorithm called the Genetic Engineering Algorithm (GEA) that draws inspiration from genetic engineering concepts. GEA redesigns the traditional GA while incorporating new search methods to isolate, purify, insert, and express new genes based on existing ones, leading to the emergence of desired traits and the production of specific chromosomes based on the selected genes. Comparative evaluations against stateof-the-art algorithms on benchmark instances demonstrate the superior performance of GEA, showcasing its potential as an innovative and efficient solution for combinatorial optimization problems.

Efficient Task Scheduling Approach in Edge-Cloud Continuum based on Flower Pollination and Improved Shuffled Frog Leaping Algorithm

The rise of edge-cloud continuum computing is a result of the growing significance of edge computing, which has become a complementary or substitute option for traditional cloud services. The convergence of networking and computers presents a notable challenge due to their distinct historical development. Task scheduling is a major challenge in the context of edge-cloud continuum computing. The selection of the execution location of tasks, is crucial in meeting the quality-of-service (QoS) requirements of applications. An efficient scheduling strategy for distributing workloads among virtual machines in the edge-cloud continuum data center is mandatory to ensure the fulfilment of QoS requirements for both customer and service provider. Existing research used metaheuristic algorithm to solve tak scheduling problem, however, must of the existing metaheuristics used suffers from falling into local mina due to their inefficiency to avoid unfeasible region in the solution search space. Therefore, there is a dire need for an efficient metaheuristic algorithm for task scheduling. This study proposed an FPA-ISFLA task scheduling model using hybrid flower pollination and improved shuffled frog leaping algorithms. The simulation results indicate that the FPA-ISFLA algorithm is superior to the PSO algorithm in terms of makespan time, resource utilization, and execution cost reduction, especially with an increasing number of tasks.

Fast random opposition-based learning Aquila optimization algorithm

Meta-heuristic algorithms are usually employed to address a variety of challenging optimization problems. In recent years, there has been a continuous effort to develop new and efficient meta-heuristic algorithms. The Aquila Optimization (AO) algorithm is a newly established swarm-based method that mimics the hunting strategy of Aquila birds in nature. However, in complex optimization problems, the AO has shown a sluggish convergence rate and gets stuck in the local optimal region throughout the optimization process. To overcome this problem, in this study, a new mechanism named Fast Random Opposition-Based Learning (FROBL) is combined with the AO algorithm to improve the optimization process. The proposed approach is called the FROBLAO algorithm. To validate the performance of the FROBLAO algorithm, the CEC 2005, CEC 2019, and CEC 2020 test functions, along with six real-life engineering optimization problems, are tested. Moreover, statistical analyses such as the Wilcoxon rank-sum test, the t-test, and the Friedman test are performed to analyze the significant difference between the proposed algorithm FROBLAO and other algorithms. The results demonstrate that FROBLAO achieved outstanding performance and effectiveness in solving an extensive variety of optimization problems.

Optimal power flow analysis with circulatory system-based optimization algorithm

Optimal power flow (OPF) is a challenging optimization problem with a large number of variables and constraints. To overcome the OPF issue, high-performance optimization algorithms are needed. In this direction, this paper has been centered on the optimization of the OPF with the circulatory system-based optimization (CSBO) algorithm. The performance of the algorithm was evaluated on the IEEE 57- and 118-bus power networks for the optimization of non-convex OPF objectives, i.e., fuel cost, power loss, voltage deviation, and enhancement of voltage stability. The solution quality of CSBO is compared with state-of-the-art metaheuristic algorithms such as Artificial Rabbits Optimization (ARO), African Vultures Optimization Algorithm (AVOA), and Chaos Game Optimization (CGO). Based on the OPF results, it is seen that the best fuel cost and voltage deviation results are calculated to be 41666.2344 $/h and 0.5871 p.u with the CSBO algorithm for the IEEE 57-bus power system. The CSBO algorithm obtained the best objective function results for the IEEE 118-bus power network with a fuel cost of 134934.3140 $/h and a power loss of 16.4688 MW. In conclusion, the present paper reports that the CSBO is a powerful and efficient metaheuristic algorithm to solve the OPF problem.

A General Scheme of a Branch-and-Bound Approach for the Sensor Selection Problem in Near-Field Broadband Beamforming.

This paper is devoted to the sensor selection problem. A broadband receiver beamforming working in a near-field is considered. The system response should be as close as possible to the desired one, which is optimized in the sense of L2 norm. The problem considered is at least NP-hard. Therefore, the branch-and-bound algorithm is developed to solve the problem. The proposed approach is universal and can be applied not only to microphone arrays but also to antenna arrays; that is, the methodology for the generation of consecutive solutions can be applied to different types of sensor selection problems. Next, for a larger microphone array, an efficient metaheuristic algorithm is constructed. The algorithm implemented is a hybrid genetic algorithm based on the ITÖ process. Numerical experiments show that the proposed approach can be successfully applied to the sensor selection problem.

An Efficient Metaheuristic algorithm for optimization for Location of Optimum Connectivity Services with Drones in Healthcare

Drones are being extensively used for a variety of practical purposes, including providing medical care. For instance, giving vaccinations, healthcare equipment, and samples of blood to individuals in isolated locations as well as during calamities. This study examined an efficient metaheuristic algorithm called MAP-LOCS, which optimizes the location of optimal connectivity amenities using drones. The use of drones in relation to facility placement and navigation is the issue. Within certain drone range limits, it entails choosing drone launch spots to optimize patient care availability. This work develops a heuristic called Location of Optimal Connection Services. The algorithm's basic premise is to start by randomly selecting a few services to open from among those that are most capable of meeting patient requests. Following that, patients are matched with the nearest open facility that can accommodate them.

A Fuzzy Bi-objective Mathematical Model for Perishable Medical Goods Supply Chain Network Considering Crisis Situations: An Empirical Study.

In case of crisis, the salvation of injuries depends on the timely provision of medical goods, relief supplies, and equipment. The aim of this study is to present a mathematical model for the supply chain network of perishable medical goods in crisis situation considering the uncertain environment. In this paper, a three-level supply chain including suppliers, intermediate warehouses, and final customers is developed for perishable medical items. The uncertainty of customer demand for service and the spent time in the intermediate warehouses are considered using the exponential distribution functions. Also, it is assumed that the life-cycle of perishable medical goods follow the Weibull distribution function. The model attempts to minimize the total costs of the supply chain and total presence time of perishable items in the whole chain. The LP-Metric method is employed for solving small-sized problems. Due to the NP-Hardness of the problem, the modified Multi-objective Particle Swarm Optimization (MOPSO) and Non-dominated Sorting Genetic Algorithm (NSGA-II) are utilized as 2 well-known and efficient meta-heuristic algorithms for solving large-sized problems. The findings indicate that the meta-heuristic algorithms are efficient in achieving close to the optimal solution for large-size problems in a reasonable time. Also, the results demonstrate that NSGA-II outperforms MOPSO in terms of the high quality solution. Finally, the applicability of the model to real-world problems is demonstrated using a real case study. This paper can assist the planners and decision-makers of perishable drugs supply chain networks in crisis conditions with on-time supplying and distributing the required emergency items.

A Scalable PDC Placement Technique for Fast and Resilient Monitoring of Large Power Grids

The Wide Area Measurement System (WAMS) is a key enabler of real-time monitoring of power grids. The essential goals of WAMS design are fast and resilient data transfer from phasor measurement units (PMU) to phasor data concentrators (PDC). We propose a scalable two-stage PDC placement technique for minimizing the end-to-end delay while maintaining resiliency. In the prescreening stage, the plausible candidates of PDC configurations are identified based on a graph theory-based multi-median function (MMF). A computationally efficient meta-heuristic algorithm is used to address scalability. In the candidate selection stage, two different algorithms, namely Suurballe's and Dijkstra's, are employed to identify the best of those plausible PDC configurations as the final design. This technique not only minimizes the hop paths between PMUs and PDCs, but also ensures network resiliency against single PMU, PDC, or communication link failure by incorporating the roles of PMUs in power grid observability into routing policy. Simulation results on the IEEE 57-bus test power system and the 2000-bus test power system demonstrate the effectiveness and scalability of the proposed technique.

Reliability-based design optimization of the spiral water jacket for motorized spindle

Spiral water jackets are essential refrigeration units in motorized spindles. The design of spiral water jackets directly affects the machining accuracy and reliability of motorized spindles. In this regard, a reliability-based design optimization (RBDO) method is presented to obtain the optimal configuration of the spiral water jacket. The proposed RBDO algorithm aims to minimize the volume of the spiral water jacket and power loss of the cooling equipment while ensuring machining reliability. The geometric correlation and machining reliability are selected as constraints. The Kriging surrogate model is established to approximate the time-consuming finite element model for improving computational efficiency. An efficient metaheuristic algorithm is introduced to perform optimal exploration for the proposed RBDO problem. The risk speed of the motorized spindle is investigated by the experiment and finite element analysis. Subsequently, the effects of various cooling configurations on thermal error are investigated thoroughly. Eventually, the proposed RBDO is conducted on a motorized spindle. Through probabilistic evaluation, machining reliability is guaranteed by the proposed RBDO compared to deterministic design optimization. This paper provides a new design paradigm for cooling configurations in motorized spindles from the perspective of machining reliability.

An Improved Light Spectrum Optimizer for Parameter Identification of Triple-Diode PV Model

Over the last few decades, researchers have paid attention to finding an effective and efficient metaheuristic algorithm that can determine the ideal parameters for PV models. In this study, to determine the TDM’s nine unknown parameters, we will examine the efficacy of a recently proposed metaheuristic algorithm called light spectrum optimizer (LSO). To further enhance the effectiveness of LSO in estimating those unknown parameters, a new improved variant called ILSO is developed. This variant employs LSO in conjunction with two newly developed update systems to improve its exploration and exploitation operators. We compare the best fitness value, worst fitness value, average fitness, standard deviation, and p-value returned by the Wilcoxon rank-sum test obtained by LSO and ILSO to those of three recently published competitors when estimating the nine unknown parameters for the Photowatt-PWP201 module and the RTC France solar cell. The experimental findings show that ILSO is the most efficient.

Medalist learning algorithm for configuration optimization of trusses

Truss configuration optimization, involving discrete sizing variables and continuous layout variables, is crucial for various practical applications. This paper introduces a highly efficient metaheuristic algorithm known as the Medalist Learning Algorithm (MLA) to address this optimization problem. Inspired by the learning behavior observed in group dynamics, the MLA offers a concise implementation procedure with two key operations: identifying medalists and facilitating individual learning. By leveraging the learning efficiency derived from a Logistic function, the MLA strikingly balances exploration and exploitation capacities throughout the learning period. To demonstrate its effectiveness, four classical truss structures with up to 44 design variables under multiple loading conditions are utilized to evaluate the MLA's performance in solving sizing and shape optimization problems of varying scales. Comparative analysis between the MLA's results and previously reported findings establishes its superiority in achieving the final feasible best weight. Statistical evidence further illustrates the MLA's consistent ability to deliver competitive solutions for a wide range of truss configuration optimization problems. The MLA's simplicity in implementation ensures its widespread applicability and potential for practical usage in the field of structural optimization.

Generalized Normal Distribution Optimization Algorithm for Economic Dispatch with Renewable Resources Integration

In an electric power system operation, the main goal of economic dispatch (ED) is to schedule the power outputs of committed generating units efficiently. This involves consideration of relevant system equality and inequality constraints to meet the required power demand at the lowest possible operational cost. This is a challenging optimization problem for power system operators that can be dealt with efficient meta-heuristic algorithms. This article uses a recent meta-heuristic approach named the generalized normal distribution optimization (GNDO) algorithm to achieve near-optimal solutions. The efficacy of the proposed GNDO algorithm is validated through experimentation on three distinct test power system networks: one with three thermal units, the second one with six thermal-unit, and the third one with ten thermal units. The algorithm's performance is also assessed on a power network with renewable energy sources. All analyses of the four test cases are conducted on the MATLAB/SIMULINK platform. Finally, this article also compares the obtained results with other literature-reported strategies, genetic algorithm (GA), particle swarm optimization (PSO), whale optimization algorithm (WOA), flower pollination algorithm (FPA), and bald eagle search (BES) algorithm. It is evident from the simulated cases that the employed GNDO algorithm exhibits superior performance for two cases and competitive performance for the remaining cases in achieving the lowest operation costs and power losses.