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Optimization of a deteriorated inventory model with bi-level credit periods and variable demand via tournament teaching learning based optimization algorithm

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2025年第5期16卷 1689-1710页

作者： Mandal, Goutam Manna, Amalesh Kumar Das, Subhash Chandra Bhunia, Asoke Kumar Univ Burdwan Dept Math Burdwan 713104 India Brainware Univ Dept Math 398 Ramkrishnapur Rd Kolkata 700125 India KIIT Deemed Be Univ Sch Appl Sci Dept Math Bhubaneswar 751024 India Chandrapur Coll Dept Math Burdwan 713145 India

Tournamenting approach plays most vital and important role for selecting best individual in knockout tournament system. This idea is used for developing hybridization of algorithm based on teaching learning based optimization technique and named as tournament teaching learning based optimization algorithm (TTLBO). The main goal of this work is to apply hybrid TTLBO for solving maximization problems corresponding to the proposed non-instantaneous inventory model for single deteriorating item with trade-credit financing, partially backlogging and Weibull distributed deterioration. Demand depends on credit period and selling price of item. Now, our aim is to determine optimal order quantity, cycle length, selling price and maximum quantity of shortage by maximizing the retailer's average profit. The validity of the developed model is tested with the help of an example. Also, the same example is solved by existing nine algorithms, viz. ABC, GQPSO, AQPSO, DE, RAO-1, RAO-2, RAO-3, HBO and TLBO algorithms to compare the performance and efficiency of the proposed TTLBO algorithm. Moreover, the analyses of sensitivity are studied to investigate the impact of different parameters involving in the model on the best found policy. Also, two nonparametric statistical tests, viz. Wilcoxon rank sum test and Friedman test are used to compare the statistical significant of proposed algorithm and existing nine algorithms. Finally, from numerical illustration and sensitivity analysis, a fruitful conclusion of this study is drawn.

关键词： Inventory Non-instantaneous deterioration Partial backlogged Tournament hybrid metaheuristic algorithm

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Two competitive hybridization approaches based on combining of Giza Pyramids Construction with Particle Swarm Optimization for solving global optimization problems

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INTELIGENCIA ARTIFICIAL-IBEROAMERICAN JOURNAL OF ARTIFICIAL INTELLIGENCE 2025年第75期28卷 114-139页

作者： Harifi, Sasan Davachi, Faraz Mohammadi, Narges Faridmohammadzadegan, Soheil Islamic Azad Univ Dept Comp Engn Karaj Branch Karaj Iran

Optimization problems are complex problems that are very difficult to solve. Although these types of problems are solved in the real world using exact methods, these methods are very time-consuming and costly. By using soft computing methods, the time and cost of problem-solving can be reduced to some extent. Engineering problems are among the complex real-world problems that can be solved through soft computing methods. One of these methods is the use of metaheuristic algorithms to optimize the solution of these types of problems. The Particle Swarm Optimization (PSO) algorithm is a common and state-of-the-art metaheuristic algorithm used to solve engineering optimization problems. This algorithm is known as swarm-based optimization techniques and has a very powerful mathematical basis. Another recently published algorithm is the Giza Pyramids Construction (GPC) algorithm. The GPC algorithm models the technological advancements of construction in ancient times. Both algorithms have many advantages through which optimization problems can be solved effectively. To increase the power of metaheuristic algorithms and solve optimization problems more effectively through them, the idea of competitive hybridization algorithms has been proposed. In this paper, two competitive hybrid approaches of combining PSO and GPC algorithms are presented. These two competitive hybridization approaches have been first applied to 45 benchmark functions and have been evaluated and analyzed statistically. Then they have been applied to six classic engineering problems. algorithms presented in each step have been compared with Genetic algorithm (GA), PSO, some recently published algorithms, and their combined approaches. The results of experiments and statistical analysis show that the solution to engineering problems has been done more effectively by using the two proposed combinations.

关键词： Optimization metaheuristic Giza Pyramids Construction algorithm Particle Swarm Optimization hybrid metaheuristic algorithm Engineering problems

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Two effective hybrid metaheuristic algorithms for minimization of multimodal functions

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INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS 2011年第11期88卷 2415-2428页

作者： Babaie-Kafaki, Saman Ghanbari, Reza Mahdavi-Amiri, Nezam Sharif Univ Technol Fac Math Sci Tehran Iran Semnan Univ Fac Sci Dept Math Semnan Iran Ferdowsi Univ Mashhad Fac Math Sci Mashhad Iran

Global optimization problems naturally arise from many applications. We propose two hybrid metaheuristic algorithms for finding a global optimum of a continuous function. Our proposed algorithms are hybridizations of genetic algorithm (GA) and variable neighbourhood search (VNS). To increase the efficiency of our algorithms, for smooth functions we present an effective locally improving line search procedure, and for non-smooth functions, we use the simplex method proposed by Nelder and Mead. By use of the recently adopted non-parametric statistical tests of Kruskal-Wallis and Mann-Whitney for analysing the behaviour of evolutionary algorithms, we compare both the efficiency and the effectiveness of our proposed algorithms with efficiently representative metaheuristic algorithms such as the multiagent GA proposed by Liang et al., the ant colony algorithm proposed by Toksari, and the VNS of Toksari and Guner on a variety of standard test functions. Computational experiments demonstrate that our proposed algorithms are efficiently effective.

关键词： unconstrained optimization variable neighbourhood search genetic algorithm hybrid metaheuristic algorithm modified line

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A Multi-objective Sustainable Medicine Supply Chain Network Design Using a Novel hybrid Multi-objective metaheuristic algorithm

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INTERNATIONAL JOURNAL OF ENGINEERING 2020年第10期33卷 1986-1995页

作者： Goodarzian, F. Hosseini-Nasab, H. Fakhrzad, M. B. Yazd Univ Dept Ind Engn Yazd Iran

End-of-life products have a severe impact on the ecological system. Potential production policies and distribution strategies for the newly manufactured product have attracted significant attention to sustainable development. Sustainability in supply chain management has much importance to achieve eco-friendly goals. In this study, we have developed sustainable objectives in the supply chain optimization framework with different constraints. The trade-off between economic, environmental and social effects objectives have identified by ensuring the optimal allocation of different products among various levels. In this regard, a new sustainability multi-objective mixed-integer linear programming mathematical model in the medicine supply chain network is developed. Although the proposed model is an NP-hard problem, we develop a novel hybrid Particle Swarm Optimization and Genetic algorithm to achieve Pareto solutions. Then, to adjust the important parameters of the algorithms and chose the optimum levels of the significant factors for more efficiency is employed the Taguchi method. The results show that the economic and environmental effects tend to be decreased and the social impacts tend to be increased in the medicine supply chain network which can exhibit the best sustainability performance. The various outcomes of numerical experiments indicate that the proposed solution algorithm is more reliable than other algorithms. The solution methods are complemented with several sensitivity analyses on the input parameters of the model.

关键词： Medicine Supply Chain Network Sustainability Mixed Integer Linear Programming Model Multi-objective Problems hybrid metaheuristic algorithm Taguchi Method

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A Quality and Distance Guided metaheuristic algorithm for Vertex Separation Problem

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IEEE ACCESS 2017年 5卷 19248-19258页

作者： Ding, Junwen Zhou, Taoqing Lu, Zhipeng Yuan, Yujin Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Hubei Peoples R China

This paper presents a quality and distance guided metaheuristic algorithm (QD-ILS) for solving the vertex separation problem. QD-ILS integrates a basic local search procedure with QD-LS strategy, which uses an augmented evaluation function that considers both solution quality and distance between the current solution and the best found solution to guide the search to explore promising regions of the search space. Assessed on two sets of 162 common benchmark instances, QD-ILS achieves highly competitive results in terms of both solution quality and computational efficiency compared with the state-of-the-art algorithms in the literature. Specifically, it improves the previous best known results for 33 out of 162 benchmark instances and matches the best known results on all except four of the remaining instances compared with the state-of-the-art algorithms in the literature. The impact of the distance and quality-based diversification strategy is also investigated.

关键词： Vertex separation problem hybrid metaheuristic algorithm quality and distance guided local search

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Scenario-based wind speed estimation using a new hybrid metaheuristic model: Particle swarm optimization and radial movement optimization

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MEASUREMENT & CONTROL 2019年第5-6期52卷 493-508页

作者： Kerem, Alper Saygin, Ali Kahramanmaras Sutcu Imam Univ Fac Engn & Architecture Dept Elect Elect Engn TR-46100 Kahramanmaras Turkey Gazi Univ Fac Technol Dept Elect & Elect Engn Ankara Turkey

This paper presents a new hybrid metaheuristic model in order to estimate wind speeds accurately. The study was started by the training process of artificial neural networks with some metaheuristic algorithms such as evolutionary strategy, genetic algorithm, ant colony optimization, probability-based incremental learning, particle swarm optimization, and radial movement optimization in the literature. The success of each model is recorded in graphs. In order to make the closest estimation and to increase the system stability, a new hybrid metaheuristic model was developed using particle swarm optimization and radial movement optimization, and the training process of artificial neural networks was performed with this new model. The data were obtained by real-time measurements from a 63-m-high wind measurement station built at the coordinates of UTM E 263254 and N 4173479, altitude 1313 m. Two different scenarios were created using actual data and applied to all models. It was observed that the error values in the designed new hybrid metaheuristic model were lower than those of the other models.

关键词： Radial movement optimization particle swarm optimization artificial neural networks wind speed estimation hybrid metaheuristic algorithm

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A hybrid PSO-SA algorithm for the travelling tournament problem

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EUROPEAN JOURNAL OF INDUSTRIAL ENGINEERING 2012年第1期6卷 2-25页

作者： Tajbakhsh, Alireza Eshghi, Kourosh Shamsi, Azam Sharif Univ Technol Dept Ind Engn Tehran Iran

Sports scheduling has become an important area of applied operations research in recent years, since satisfying fans and teams' requests and revenues of a sports league and TV networks may be affected by quality of the league schedule. While this type of scheduling problem can be solved by mathematical methods and exact solutions are accessible, it computationally leads to hard problems. The travelling tournament problem (TTP) is defined as minimising total travelling distance for all teams in a league. In this study, a new mathematical model for the TTP with the no-repeater constraint is presented. In addition, a very fast hybrid metaheuristic algorithm is proposed, which combines particle swarm optimisation (PSO) and simulated annealing (SA). Our computational experiments on standard instances show that the hybrid approach results in comparable to or even better than current best known solutions, specifically in computational time. [Received 24 August 2009;Revised 16 January 2010, 9 June 2010;Accepted 12 June 2010]

关键词： sports scheduling minimising total travelling distance travelling tournament problem TTP particle swarm optimisation PSO simulated annealing SA hybrid metaheuristic algorithm

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An efficient hybrid algorithm based on Water Cycle and Moth-Flame Optimization algorithms for solving numerical and constrained engineering optimization problems

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SOFT COMPUTING 2019年第5期23卷 1699-1722页

作者： Khalilpourazari, Soheyl Khalilpourazary, Saman Kharazmi Univ Dept Ind Engn Fac Engn Tehran Iran Urmia Univ Technol Dept Mech Engn Fac Engn Orumiyeh Iran

This paper proposes a hybrid algorithm based on Water Cycle and Moth-Flame Optimization algorithms for solving numerical and constrained engineering optimization problems. The spiral movement of moths in Moth-Flame Optimization algorithm is introduced into the Water Cycle algorithm to enhance its exploitation ability. In addition, to increase randomization in the new hybrid method, the streams in the Water Cycle algorithm are allowed to update their position using a random walk (Levy flight). The random walk significantly improves the exploration ability of the Water Cycle algorithm. The performance of the new hybrid Water Cycle-Moth-Flame Optimization algorithm (WCMFO) is investigated in 23 benchmark functions such as unimodal, multimodal and fixed-dimension multimodal benchmark functions. The results of the WCMFO are compared to the other state-of-the-art metaheuristic algorithms. The results show that the hybrid method is able to outperform the other state-of-the-art metaheuristic algorithms in majority of the benchmark functions. To evaluate the efficiency of the WCMFO in solving complex constrained engineering and real-life problems, three well-known structural engineering problems are solved using WCMFO and the results are compared with the ones of the other metaheuristics in the literature. The results of the simulations revealed that the WCMFO is able to provide very competitive and promising results comparing to the other hybrid and metaheuristic algorithms.

关键词： Numerical optimization Water cycle algorithm Moth-flame optimization Constrained engineering optimization hybrid metaheuristic algorithm

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Refining breast cancer biomarker discovery and drug targeting through an advanced data-driven approach

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BMC BIOINFORMATICS 2024年第1期25卷 33页

作者： Rakhshaninejad, Morteza Fathian, Mohammad Shirkoohi, Reza Barzinpour, Farnaz Gandomi, Amir H. Iran Univ Sci & Technol Ind Engn Dept Hengam St Tehran *** Iran Univ Tehran Med Sci Imam Khomeini Hosp Complex Canc Biol Res Ctr Canc Inst Keshavarz Blvd Tehran *** Iran Univ Technol Sydney Fac Engn & Informat Technol Ultimo NSW 2007 Australia Obuda Univ Univ Res & Innovat Ctr EKIK H-1034 Budapest Hungary

Breast cancer remains a major public health challenge worldwide. The identification of accurate biomarkers is critical for the early detection and effective treatment of breast cancer. This study utilizes an integrative machine learning approach to analyze breast cancer gene expression data for superior biomarker and drug target discovery. Gene expression datasets, obtained from the GEO database, were merged post-preprocessing. From the merged dataset, differential expression analysis between breast cancer and normal samples revealed 164 differentially expressed genes. Meanwhile, a separate gene expression dataset revealed 350 differentially expressed genes. Additionally, the BGWO_SA_Ens algorithm, integrating binary grey wolf optimization and simulated annealing with an ensemble classifier, was employed on gene expression datasets to identify predictive genes including TOP2A, AKR1C3, EZH2, MMP1, EDNRB, S100B, and SPP1. From over 10,000 genes, BGWO_SA_Ens identified 1404 in the merged dataset (F1 score: 0.981, PR-AUC: 0.998, ROC-AUC: 0.995) and 1710 in the GSE45827 dataset (F1 score: 0.965, PR-AUC: 0.986, ROC-AUC: 0.972). The intersection of DEGs and BGWO_SA_Ens selected genes revealed 35 superior genes that were consistently significant across methods. Enrichment analyses uncovered the involvement of these superior genes in key pathways such as AMPK, Adipocytokine, and PPAR signaling. Protein-protein interaction network analysis highlighted subnetworks and central nodes. Finally, a drug-gene interaction investigation revealed connections between superior genes and anticancer drugs. Collectively, the machine learning workflow identified a robust gene signature for breast cancer, illuminated their biological roles, interactions and therapeutic associations, and underscored the potential of computational approaches in biomarker discovery and precision oncology.

关键词： Breast cancer Machine learning Biomarker discovery Ensemble method hybrid metaheuristic algorithm

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A quality and distance guided hybrid algorithm for the vertex separator problem

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COMPUTERS & OPERATIONS RESEARCH 2017年 78卷 255-266页

作者： Ding, Junwen Lu, Zhipeng Zhou, Taoqing Xu, Liping Huazhong Univ Sci & Technol Sch Comp Sci & Technol SMART Wuhan 430074 Peoples R China

This paper presents a quality and distance guided local search (QD-LS) as the diversification strategy for metaheuristics. QD-LS uses an augmented evaluation function which considers both solution quality and distance between the current solution and the best found solution to guide the search towards promising regions of the search space. To evaluate the performance of QD-LS, we propose a quality and distance guided hybrid algorithm (QD-HA) for solving the vertex separator problem. Based on the framework of evolutionary algorithms, QD-HA integrates a basic tabu search procedure with a random greedy recombination operator and QD-LS strategy. Assessed on two sets of 348 common benchmark instances, QD-HA achieves highly competitive results in terms of both solution quality and computational efficiency compared with state-of-the-art algorithms in the literature. Specifically, it improves the previous best known results for 63 out of 244 large instances while matching the best known results for others. The impact of the quality and distance based diversification strategy is also investigated.

关键词： Vertex separator problem hybrid metaheuristic algorithm Distance and quality guided tabu search Diversification mechanism

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