Efficient Metaheuristic Algorithm Research Articles

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 qualitative analysis experiments 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 to various function types. 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 fifteen 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 comparative findings emphasize AO's efficacy and its potential in real-world optimization applications. The source codes of this paper will be available in https://aliasgharheidari.com/AO.html and other public websites.

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 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 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.

Dynamic Offloading Technique for Latency-Sensitive Internet of Things Applications using Fog Computing

Internet of Things (IoT) has evolved as a novel paradigm that provides com-putation power to different entities connected to it. IoT offers services to multiple sectors such as home automation, industrial automation, traffic management, healthcare sector, agriculture industry etc. IoT generally relies on cloud data centers for extended analytics, processing and storage support. The cloud offers highly scalable and robust platform for IoT applications. But latency sensitive IoT applications suffer delay issues as the cloud lies in remote location. Edge/fog computing was introduced to overcome the issues faced by delay-sensitive IoT applications. These platforms lie close to the IoT network, reducing the delay and response time. The fog nodes are usually distributed in nature. The data has to be properly offloaded to available fog nodes using efficient strategies to gain benefit from the integration. Differ-ent offloading schemes are available in the literature to overcome this prob-lem This paper proposes a novel offloading approach by combining two effi-cient metaheuristic algorithms, Honey Badger Algorithm (HBA) and Fla-mingo Search Algorithm (FSA) termed as HB-FS algorithm. The HB-FS is executed in an iterative manner optimizing the objective function in each it-eration. The performance evaluation of the proposed approach is done with different existing metaheuristic algorithms and the evaluations show that the proposed work outperforms the existing algorithms in terms of latency, response time and execution time. The methodology also offers better degree of imbalance with proper load balancing under different conditions.

New binary archimedes optimization algorithm and its application

Optimization problem, as a hot research field, is applied to many industries in the real world. Due to the complexity of different search spaces, metaheuristic optimization algorithms are proposed to solve this problem. As a recently introduced optimization method inspired by physics, Archimedes Optimization Algorithm (AOA) is an efficient metaheuristic algorithm based on Archimedes' law. It has the advantages of fast convergence speed and balance between local and global search ability when solving continuous problems. However, discrete problems exist more in practical applications. AOA needs to be further improved in dealing with such problems. On this basis, to make Archimedes Optimization Algorithm better applied to solve discrete problems, a Binary Archimedes Optimization Algorithm (BAOA) is proposed in this paper, which incorporates a novel V-shaped transfer function. The proposed method applies the BAOA to COVID-19 classification of medical data, segmentation of real brain lesion, and the knapsack problem. The experimental results show that the proposed BAOA can solve the discrete problem well.

An Efficient Metaheuristic Algorithm for Job Shop Scheduling in a Dynamic Environment

This paper proposes an Improved Multi-phase Particle Swarm Optimization (IMPPSO) to solve a Dynamic Job Shop Scheduling Problem (DJSSP) known as an non-deterministic polynomial-time hard (NP-hard) problem. A cellular neighbor network, a velocity reinitialization strategy, a randomly select sub-dimension strategy, and a constraint handling function are introduced in the IMPPSO. The IMPPSO is used to solve the Kundakcı and Kulak problem set and is compared with the original Multi-phase Particle Swarm Optimization (MPPSO) and Heuristic Kalman Algorithm (HKA). The results show that the IMPPSO has better global exploration capability and convergence. The IMPPSO has improved fitness for most of the benchmark instances of the Kundakcı and Kulak problem set, with an average improvement rate of 5.16% compared to the Genetic Algorithm-Mixed (GAM) and of 0.74% compared to HKA. The performance of the IMPPSO for solving real-world problems is verified by a case study. The high level of operational efficiency is also evaluated and demonstrated by proposing a simulation model capable of using the decision-making algorithm in a real-world environment.

Accelerated Particle Swarm Optimization Algorithms Coupled with Analysis of Variance for Intelligent Charging of Plug-in Hybrid Electric Vehicles

Plug-in hybrid electric vehicles (PHEVs) and plug-in electric vehicles (PEVs) have gained enormous attention for their ability to reduce fuel consumption in transportation and are, thus, helpful in the reduction of the greenhouse effect and pollution. However, they bring up some technical problems that should be resolved. Due to the ever-increasing demand for these PHEVs, the simultaneous connection of large PEVs and PHEVs to the electric grid can cause overloading, which results in disturbance to overall power system stability and quality and can cause a blackout. Such situations can be avoided by adequately manipulating power available from the grid and vehicle power demand. State of charge (SoC) is the leading performance parameter that should be optimized using computational techniques to charge vehicles efficiently. In this research, an efficient metaheuristic algorithm, accelerated particle swarm optimization (APSO), and its five variants were applied to allocate power to vehicles connected to the grid intelligently. For this, the maximization of average SoC is considered a fitness function, and each PHEV can be connected to the grid once a day so that the maximum number of cars can be charged daily. To statistically compare the performance of these six algorithms, one-way ANOVA was used. Simulation and statistical results obtained by maximizing this highly non-linear objective function show that accelerated particle swarm optimization with Variant 5 achieved some improvements in terms of computational time and best fitness value. The APSO-5 solution has a considerable percentage increase compared with the solution of other variants of APSO for the four PHEV datasets considered. Moreover, after 30 trials, APSO 5 gives the highest possible fitness value among all the algorithms.

A two-echelon location routing problem considering sustainability and hybrid open and closed routes under uncertainty

Location-routing is an extremely important problem in supply chain management. In the location-routing problem, decisions are made about the location of facilities such as distribution centers as well as the set of vehicle routes. Today, organizations seek to reduce the transportation cost by outsourcing leading to a particular kind of transportation problems known as open routing. However, the increasing attention to environment have led to paying more attention to environmental issues and reducing the environmental impacts of logistics activities. To this end, in this paper, both open and closed routes were simultaneously addressed by developing a multi-objective mixed integer linear programming model that included three economic, environmental, and social responsibility aspects. The three objective functions of the proposed model encompass the minimization of total costs and greenhouse gas emissions, and the maximization of employment rate and economic development. Also, in this study, a different type of routing was considered in each echelon. A small-sized problem instance was solved using the Augmented Epsilon Constraint (AEC) method with the CPLEX Optimizer Solver for the validation of the proposed model. Moreover, the sensitivity analysis was performed to investigate the effect of changing main parameters on the values of the objective function. Due to the NP-Hardness of the problem, two efficient metaheuristic algorithms of Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Stochastic Fractal Search (MOSFS) were exploited to solve the medium and large size problems. The performance of the algorithms was compared on the basis of six different well-known indexes of Time, MID, RAS, Diversity, Spacing, and SNS. According to the obtained results, the performance of the MOSFS algorithm was %20, %9, %11.22, %10.03, and %19.06 higher than the performance of the NSGA-II on the basis of SNS, RAS, MID, Diversity, and Time indexes, respectively. On the other hand, the NSGA-II performance was %6.3 higher than the MOSFS performance in terms of Spacing index.

Multifrequency-based tension intelligent identification for cables with unknown end-restraints using a metaheuristic algorithm

Vibration frequency methods are widely used for cable tension identification; however, calibrated a priori knowledge of the flexural stiffness, axial stiffness and end-restraints of cables cannot always be obtained in advance, leading to the inabilities of these methods in the identification from theoretical or empirical formulations. In this study, a multifrequency-based intelligent multiparameter identification method is proposed for the tension identification of a cable equipped with a single sensor. The framework of the method establishes an optimization objective function with the theoretical and on-site frequencies based on a cable dynamic model considering the effects of inclination, sagging, flexural stiffness and end-restraints. The identification domain is obtained from empirical formulas and dimensionless characteristic parameters, and the model is solved using a finite difference method. An efficient metaheuristic algorithm is introduced as an engine for the simultaneous identification of the cable tension and the parameters supposed to be calibrated first. The accuracy and efficiency of the method are demonstrated through comparative studies consisting of algorithm tests, tension identification tests of laboratory rigid short cables and some stay-cables of an on-site bridge.