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An efficient Optimization State-based Coyote Optimization Algorithm and its applications

APPLIED SOFT COMPUTING 2023年 147卷

作者： Zhang, Qingke Bu, Xianglong Zhan, Zhi-Hui Li, Junqing Zhang, Huaxiang Shandong Normal Univ Sch Informat Sci & Engn Jinan 250358 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China

Coyote Optimization Algorithm (COA) has demonstrated efficient performance by utilizing the multiple pack (subpopulation) mechanism. However, the fixed number of packs and a relatively singular evolutionary strategy limit its comprehensive optimization performance. Thus, this paper proposes a COA variant, referred to as the Optimization State-based Coyote Optimization Algorithm (OSCOA). In the OSCOA algorithm, a Population Optimization State Estimation Mechanism is employed for estimating the current population optimization state. Then, the estimation result is used to guide the algorithm in setting the number of packs appropriately as well as selecting appropriate evolutionary strategies to refine search directions, thereby avoiding blind exploration. Additionally, the estimation result assists each pack in selecting suitable parents to generate pups, further improving the global search efficiency of the algorithm. To validate the effectiveness of the proposed algorithm, the OSCOA algorithm is subjected to comprehensive testing and analysis along with seven efficient optimizers on 71 benchmark functions derived from the CEC2014, CEC2017, and CEC2022 benchmark suites. The results of these extensive experiments indicate the competitive performance of OSCOA. Furthermore, to further assess the capability of the OSCOA algorithm in addressing real-world problems, two practical applications is considered: wireless sensor network deployment and image segmentation. The outcomes of these applications further confirm the efficacy and stability of the OSCOA algorithm in tackling real-world scenarios.

关键词： meta-heuristics algorithms Coyote Optimization Algorithm Population state estimation Multi-thresholding image segmentation Deployment problems of wireless sensor networks

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Spatial prediction of human brucellosis susceptibility using an explainable optimized adaptive neuro fuzzy inference system

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ACTA TROPICA 2024年 260卷 107483页

作者： Jafari, Ali Alesheikh, Ali Asghar Zandi, Iman Lotfata, Aynaz K N Toosi Univ Technol Fac Geodesy & Geomatics Engn Dept GIS Tehran Iran Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Dept GIS Tehran Iran Univ Calif Davis Sch Vet Med Dept Pathol Microbiol & Immunol Davis CA USA

Brucellosis, a zoonotic disease caused by Brucella bacteria, poses significant risks to human, livestock, and wildlife health, alongside economic losses from livestock morbidity and mortality. This study improves Human Brucellosis Susceptibility Mapping (HBSM) by integrating the Adaptive Neuro-Fuzzy Inference System (ANFIS) with meta-heuristic algorithms, including Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Boruta-XGBoost identified key covariates, while VIF and tolerance tests addressed collinearity, and Shapley additive explanation (SHAP) values enhanced model interpretability. In Mazandaran province, Iran (2012-2018), the hybrid ANFIS-PSO model demonstrated superior performance (RMSE: 0.5076;R2: 0.6980). SHAP analysis highlighted mean elevation, NDVI, and relative humidity as the most impactful covariates, while max evaporation and precipitation had minimal influence. ANFIS-based models outperformed Support Vector Regression (SVR), offering a robust framework for brucellosis control. This approach enables effective interventions and resource allocation, with potential for improvement through advanced algorithms and greater interpretability.

关键词： Human brucellosis susceptibility mapping Adaptive neuro-fuzzy inference system meta-heuristics algorithms Shapley additive explanation

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Designing a sustainable bioethanol supply chain network: A combination of machine learning and meta-heuristic algorithms

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INDUSTRIAL CROPS AND PRODUCTS 2022年 189卷

作者： Momenitabar, Mohsen Ebrahimi, Zhila Dehdari Ghasemi, Peiman North Dakota State Univ Coll Business Dept Transportat Logist & Finance Fargo ND 58105 USA Univ Vienna Dept Business Decis & Analyt Kolingasse 14-16 A-1090 Vienna Austria

Bioethanol demands have increased during the last decade due to unexpected events worldwide. It is among the renewable energy sources that are utilized to replace fossil-fuel-based energy. Designing an efficient, Sustainable Bioethanol Supply Chain Network (SBSCN) is a critical task for the government and communities to manage the bioethanol demands appropriately. The contribution of this study is threefold. First, this study project the demand using three popular machine learning methods, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Ensemble Learning (Adaboost) methods, to find the best one to be utilized as an input to the proposed mathematical to find optimal values for strategic, planning, and tactical decision variables. Second, the unemployment rate is considered an important parameter of the model to maximize the social effects. Third, since the proposed model of this study is NP-hard, to solve the problem, the CPLEX solver is applied for small size and two meta-heuristic algorithms, including Non-dominated Sorting Genetic Algorithm II (NSGA-II) and MultiObjective Invasive Weed Optimization (MOIWO) are considered for medium and large size problems to find Pareto optimal solutions. Due to the sensitivity of two meta-heuristics algorithms, the Taguchi method, such as Max Spread (MS) and Mean Ideal Distance (MID), is utilized to control the parameters of those two applied algorithms. Since North Dakota (ND) is among the states with the most potential to produce bioethanol due to its vast land area, including marginal cropland and Conservation Research Program (CRP), the proposed model has been evaluated and validated based on the ND case study. The results show that the MOIWO algorithm outperforms NSGA-II based on the proposed model and the case study of this paper. Also, this algorithm is more reliable in terms of solution quality to tackle the problem. Finally, some research directions are discussed for future studies, and managerial

关键词： Sustainable supply chain network Machine learning methods Bioethanol meta-heuristics algorithms Demand projection Taguchi method

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Transportation scheduling optimization by a collaborative strategy in supply chain management with TPL using chemical reaction optimization

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NEURAL COMPUTING & APPLICATIONS 2020年第8期32卷 3649-3674页

作者： Islam, Md Rafiqul Mahmud, Md Riaz Pritom, Rayhan Morshed Khulna Univ Comp Sci & Engn Discipline Khulna 9208 Bangladesh

Optimization of supply chain management is a way of ensuring the usability of resources and related technologies at the best possible way. Transportation scheduling of vehicle and transportation nodes in supply chain management is an important factor in order to create a stable chained network by ensuring the highest amount of product distribution and lowest logistics cost. In recent years, a number of programming models like linear programming, heuristics and meta-heuristics optimization approaches are proposed by the researchers to solve this combinatorial NP-hard problem. In this paper, we have studied and analyzed the nature of transportation vehicle scheduling problem in a supply chain network with the help of third-party logistics enterprise by using a meta-heuristic algorithm called chemical reaction optimization (CRO). At first, we have classified all the transportation nodes into three distinct classifications. Then, a collaborative transportation scheduling strategy is used which is based on two significant kinds of transportation nodes. For the first two kinds of nodes, we have randomly created a large number of combined transportation routes, and the vehicle scheduling for the last standalone nodes is created by a random matrix generation. Then, we have proposed a CRO algorithm using four reaction operators with an additional repair operator to find out the best transportation routes within shortest computing time. We named our proposed algorithm as chemical reaction optimization for supply chain management (CRO-SCM). The proposed CRO-SCM algorithm is analyzed with the standard dataset from the proposed model using modified ACO-NSO algorithm which is the state of the art. In addition, a random dataset of different scales of transportation nodes is considered to evaluate the efficiency of the algorithm. Moreover, six different scales of problem sets consisting different number of nodes are adopted to analyze the performance of the proposed CRO algorithm.

关键词： Supply chain management Third-party logistics Collaborative transportation scheduling Random matrix generation Chemical reaction optimization meta-heuristics algorithms

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Optimized task scheduling on fog computing environment using meta heuristic algorithms 4

Optimized task scheduling on fog computing environment using...

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4th IEEE International Conference on Smart Cloud (IEEE SmartCloud) / 3rd IEEE International Symposium on Reinforcement Learning (IEEE ISRL)

作者： Jayasena, K. P. N. Thisarasinghe, B. S. Sabaragamuwa Univ Sri Lanka Dept Comp & Informat Syst Belihuloya Sri Lanka

ISBN: (纸本)9781728155050

Fog Computing paradigm extends the cloud computing technology to the edge of the computer network. The basic concept is kind of similar to cloud computing and supports virtualizations as well. It is very useful in healthcare application, intelligent transportation systems, financial and smart cities. Optimal task scheduling is an important topic in fog computing virtualization. The task scheduling procedure is an NP-complete problem where the time needed to locate the solution varies by the size of the problem. There are various computation-based performance metrics use in scheduling procedure such as energy consumption and execution cost. Optimal task scheduling of tasks in fog computing can be classified as heuristic, meta-heuristics and hybrid task scheduling approaches. The heuristic task scheduling algorithms deliver ease to schedule the task and deliver the best possible solutions, but it doesn't guarantee the optimal result. The meta-heuristics approaches can handle massive search space to discover better optimal solution for task scheduling problem within reasonable time. Smart healthcare application model is implemented and simulated in iFogSim simulator tool which is used to test and select the technique to introduce a Whale Optimization algorithm. Whale Optimization algorithm is compared with several heuristic algorithms (RR, SJF) and PSO meta-heuristic algorithm. The results show that proposed algorithm improved the average energy consumption of 4.47% and cost 62.07% relative to the RR, SJF algorithms and energy consumption of 4.50% and cost 60.91% relative to the PSO algorithm.

关键词： Fog Computing task scheduling iFogsim Energy efficiency Whale Optimization meta-heuristics algorithms

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ASCA-PSO: Adaptive sine cosine optimization algorithm integrated with particle swarm for pairwise local sequence alignment

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EXPERT SYSTEMS WITH APPLICATIONS 2018年 99卷 56-70页

作者： Issa, Mohamed Hassanien, Aboul Ella Oliva, Diego Helmi, Ahmed Ziedan, Ibrahim Alzohairy, Ahmed Zagazig Univ Fac Engn Comp Engn & Syst Dept Zagazig Egypt Cairo Univ Fac Comp & Informat Cairo Egypt Univ Guadalajara Dept Ciencias Computac Guadalajara Mexico Zagazig Univ Fac Agr Genet Dept Zagazig Egypt

The sine cosine algorithm (SCA), a recently proposed population-based optimization algorithm, is based on the use of sine and cosine trigonometric functions as operators to update the movements of the search agents. To optimize performance, different parameters on the SCA must be appropriately tuned. Setting such parameters is challenging because they permit the algorithm to escape from local optima and avoid premature convergence. The main drawback of the SCA is that the parameter setting only affects the exploitation of the prominent regions. However, the SCA has good exploration capabilities. This article presents an enhanced version of the SCA by merging it with particle swarm optimization (PSO). PSO exploits the search space better than the operators of the standard SCA. The proposed algorithm, called ASCA-PSO, has been tested over several unimodal and multimodal benchmark functions, which show its superiority over the SCA and other recent and standard meta-heuristic algorithms. Moreover, to verify the capabilities of the SCA, the SCA has been used to solve the real-world problem of a pairwise local alignment algorithm that tends to find the longest consecutive substrings between two biological sequences. Experimental results provide evidence of the good performance of the ASCA-PSO solutions in terms of accuracy and computational time. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Sine cosine algorithm (SCA) Particle swarm optimization (PSO) meta-heuristics algorithms Pairwise local alignment Longest consecutive substrings Smith-Waterman alignment algorithm

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Dual-arm cooperative manipulation based on differential evolution

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2019年第1期16卷

作者： Hernandez-Barragan, Jesus Lopez-Franco, Carlos Alanise, Alma Y. Arana-Daniel, Nancy Lopez-Franco, Michel Univ Guadalajara Dept Comp Sci Guadalajara Jalisco Mexico

Cooperative manipulation in dual-arm robotic systems is a fundamental capability to perform many problems such as human-like tasks. In this article, we present an approach to solve dual-arm cooperative manipulation tasks using the differential evolution algorithm. In this work, manipulator kinematics are represented using the Denavit-Hartenberg model. The proposed method is able to avoid singularities because it does not require the inversion of any Jacobian matrix. In addition, the proposed approach handles joint limit constraints based on penalty functions. As a final remark, this approach is suitable for robotic systems of redundant or non-redundant serial manipulators composed of revolute or prismatic joints. Simulation experiments illustrate the effectiveness of the proposed approach under different dual-arm configurations. Furthermore, real experiments were performed using a dual-arm KUKA Youbot system to show the applicability of the proposed approach.

关键词： meta-heuristics algorithms differential evolution path tracking dual-arm manipulation cooperative manipulators

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Optimum seismic design of steel frames considering the connection types

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JOURNAL OF CONSTRUCTIONAL STEEL RESEARCH 2017年第Mar.期130卷 79-87页

作者： Kaveh, A. Ghafari, M. H. Gholipour, Y. Iran Univ Sci & Technol Ctr Excellence Fundamental Studies Struct Engn POB 16846-13114 Tehran Iran Univ Tehran Engn Optimizat Res Grp Tehran Iran

In this paper seismic design optimization of steel moment frames is studied. In addition to element sections, the connection types (simple or rigid) are considered as design variables. Element stresses and story drifts are limited according to the AISC-LRFD design criteria. The heights of the bottom and top columns, heights of the two intersecting beams and the flange widths of beams and columns are compared at each joint in order to control the constructional requirements. Compactness and slenderness for sections and column to beam plastic moment capacity ratio for joints are checked according to seismic provisions of AISC. Optimum results of three examples are obtained utilizing the enhanced colliding bodies optimization and particle swarm optimization and the performance of these algorithms for the present optimization problem are compared. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Structural optimization Steel moment frame Frame connection topology Structural seismic design meta-heuristics algorithms

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A Comparative Study of Firefly Algorithm and Shuffled Frog-leaping Algorithm for Constrained Free and Manufacturing Optimization Problems

A Comparative Study of Firefly Algorithm and Shuffled Frog-l...

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2nd International Conference on Frontiers of Mechanical Engineering and Materials Engineering (MEME 2013)

作者： Aungkulanon, Pasura Srikun, Isaree Ruekkasem, Lakkana Phranakorn Rajabhat Univ Fac Ind Technol Bangkok 10220 Thailand

ISBN: (纸本)9783037859247

Manufacturing process problems in industrial systems are currently large and complicated. The effective methods for solving these problems using a finite sequence of instructions can be classified into two groups;optimization and meta-heuristic algorithms. In this paper, a well-known meta-heuristic approach called Firefly Algorithm was used to compare with Shuffled Frog-leaping Algorithm. All algorithms were implemented and analyzed with manufacturing process problems under different conditions, which consist of single, multi-peak and curved ridge optimization. The results from both methods revealed that Firefly Algorithm seemed to be better in terms of the mean and variance of process yields including design points to achieve the final solution.

关键词： Optimization meta-heuristics algorithms Firefly Algorithm Shuffled Frog-Leaping Algorithm Manufacturing Optimization Problems

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