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KMOEA: A Knowledge-Based multiobjective algorithm for Distributed Hybrid Flow Shop in a Prefabricated System

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2022年第8期18卷 5318-5329页

作者： Li, Jun-Qing Chen, Xiao-Long Duan, Pei-Yong Mou, Jian-Hui Shandong Normal Univ Sch Informat & Engn Jinan 250014 Peoples R China Liaocheng Univ Sch Comp Sci Liaocheng 252059 Shandong Peoples R China Yantai Univ Sch Math & Informat Sci Yantai 264005 Peoples R China Yantai Univ Sch Mech Elect & Automot Engn Yantai 264005 Peoples R China

In this article, a distributed hybrid flow shop scheduling problem with variable speed constraints is considered. To solve it, a knowledge-based adaptive reference points multiobjective algorithm (KMOEA) is developed. In the proposed algorithm, each solution is represented with a 3-D vector, where the factory assignment, machine assignment, operation scheduling, and speed setting are encoded. Then, four problem-specific lemmas are proposed, which are used as the knowledge to guide the main components of the algorithm, including the initialization, global, and local search procedures. Next, an efficient initialization approach is presented, which is embedded with several problem-related initialization rules. Furthermore, a novel Pareto-based crossover heuristic is designed to learn from more promising solutions. To enhance the local search abilities, a speed adjustment local search method is investigated. Finally, a set of instances generated based on the realistic prefabricated production system is tested to verify the efficiency and effectiveness of the proposed algorithm.

关键词： Production facilities Production Casting Job shop scheduling Energy consumption Heuristic algorithms Power demand Distributed hybrid flow shop energy consumption multiobjective algorithm prefabricated system variable speed

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Robust and Reliable Design Alternatives to Water Distribution Networks: Introducing a Penalty-Free Hybrid Metaheuristic multiobjective algorithm with a Posterior Performance Investigation Model

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JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT 2024年第12期150卷 04024055-04024055页

作者： Poojitha, S. N. Jothiprakash, V. Natl Inst Technol Calicut Dept Civil Engn Kozhikode 673601 Kerala India Indian Inst Technol Dept Civil Engn Mumbai 400076 Maharashtra India

The present study introduces a novel penalty-free hybrid metaheuristic, differential evolution-krill herd algorithm (DE-KHA) a multiobjective algorithm (MOA) for the reliability-based optimal design of water distribution networks (WDNs). The selection mechanism of the DE-KHA MOA is equipped with nondominated sorting and density estimation schemes to obtain a Pareto front with diverse trade-off solutions. The introduced penalty-free scheme allows the algorithm to search for hydraulically functional nondominant solutions. With this implemented framework of the DE-KHA MOA, the WDN design problem is formulated with two conflicting objectives, (1) minimizing pipe investment cost;and (2) maximizing network's reserve through flow entropy (SF), a surrogate reliability measure. The application and computational efficiency of the proposed MOA are demonstrated by employing two well-established benchmark case studies. Parallelly a trial-based approach for conducting sensitivity analysis for MOAs is demonstrated. The computational results manifest the efficacy of the penalty-free DE-KHA MOA in resulting in the Pareto front comprising the hydraulic feasible nondominant solutions of disparate trade-off relationships. As well, the results highlight the applicability of the proposed sensitivity analysis approach in improving the convergence behavior of the MOA. Following the reliability-based design, to assess the flexibility of the solved nondominant solutions under critical scenarios above design standards, the study performed a posterior performance investigation using pressure-driven analysis. The results demonstrate the effectiveness of the proposed approach with supported subjective knowledge in selecting robust design alternatives that are mechanically and hydraulically reliable. Moreover, the proposed approach eases the effort and perplexing state of handling increased nondominant design options with larger-size WDNs. The present study introduces a computationally efficient

关键词： Hydraulic uncertainties multiobjective algorithm Penalty-free mechanism Posterior performance investigation Sensitivity analysis Reliability-based design Robust design alternatives

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Grid-based multi-objective cheetah optimization for engineering applications

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2025年第4期28卷 1-27页

作者： Sharma, Shubhkirti Kumar, Vijay Natl Inst Technol Dept Comp Sci & Engn Hamirpur 177001 HP India Dr BR Ambedkar Natl Inst Technol Dept Informat Technol Jalandhar 144011 Punjab India

This paper introduces a novel Grid-based Multi-Objective Cheetah Optimizer (MCO) algorithm for engineering applications. The MCO algorithm is derived from the hunting strategy of cheetahs and builds upon its single-objective predecessor, the Cheetah Optimizer (CO). The MCO employs a combination of non-dominated sorting, a grid mechanism, and an archive to maintain and distribute solutions effectively. Firstly, non-dominance selection is applied to identify the best set of solutions. Secondly, an archive is maintained to preserve these optimal solutions. Thirdly, a grid-based method ensures a better distribution and selection of the solution set. The viability of the proposed MCO algorithm is verified through simulation studies on twenty two benchmark test functions and five engineering problems, evaluated against five performance metrics. Comparative analysis with five well-established multi-objective algorithms demonstrates that the MCO algorithm surpasses these in terms of achieving closer approximations to the Pareto front. The results confirm that the MCO algorithm can provide a diverse and effective set of optimal solutions, making it a superior choice for complex engineering problems requiring multi-objective optimization.

关键词： Multi-objective cheetah optimizer multiobjective algorithm Multi-objective optimization Meta-heuristics Optimization

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multiobjective Program and Hybrid Imperialist Competitive algorithm for the Mixed-Model Two-Sided Assembly Lines Subject to Multiple Constraints

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2018年第1期48卷 119-129页

作者： Li, Dashuang Zhang, Chaoyong Tian, Guangdong Shao, Xinyu Li, Zhiwu Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Hubei Peoples R China Jilin Univ Transportat Coll Changchun 130022 Jilin Peoples R China Macau Univ Sci & Technol Inst Syst Engn Macau 999078 Peoples R China Xidian Univ Sch Electromech Engn Xian 710071 Shaanxi Peoples R China

A mixed-model two-sided assembly line is a manufacturing system designed for the production of large-sized products. In order to describe the actual condition, this paper presents a novel multiobjective programming model for balancing a mixed-model two-sided assembly line subject to multiple constraints, in which, additional constraints including zoning, synchronous, and positional constraints are considered besides the traditional constraints, e.g., the precedence constraint. Two objectives are simultaneously to be optimized, one is to minimize the combination of the weighted line efficiency and the weighted smoothness index, and the other is to minimize the weighted total relevant costs per unit of a product. A novel multiobjective hybrid imperialist competitive algorithm (MOHICA) is proposed to solve this problem. In the presented MOHICA, the sigma method is employed to quantify every individual, a novel merging method is introduced to reserve better individuals into the evolutionary population, and late acceptance hill-climbing (LAHC) algorithm is presented as a local search algorithm to achieve accurate balance between intensification and diversification. The experimental results on the selected benchmark instances and a practical case show that the proposed multiobjective algorithm outperforms nondominated sorting genetic algorithm (NSGA)-II, multiobjective improved teaching-learning-based optimization, and NSGA-III existing in the literature.

关键词： two-sided assembly line multiobjective algorithm late acceptance hill-climbing (LAHC) Imperialist competitive algorithm (ICA)

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multiobjective Placement of Edge Servers in MEC Environment Using a Hybrid algorithm Based on NSGA-II and MOPSO

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IEEE INTERNET OF THINGS JOURNAL 2024年第18期11卷 29819-29837页

作者： Bahrami, Bahareh Khayyambashi, Mohammad Reza Mirjalili, Seyedali Univ Isfahan Fac Comp Engn Dept Comp Architecture Esfahan *** Iran Univ Isfahan Fac Comp Engn Dept Software Engn Esfahan *** Iran Torrens Univ Australia Brisbane Ctr Artificial Intelligence Res & Optimisat Fortitudevalley Qld 4006 Australia Obuda Univ Univ Res & Innovat Ctr H-1034 Budapest Hungary VSB TU Ostrava Fac Elect Engn & Comp Sci Ostrava 70080 Czech Republic

In a mobile edge computing (MEC) environment, latency and energy consumption can be reduced by offloading tasks from mobile devices to edge servers (ESs) instead of remote cloud servers. The placement of ESs closest to end users can improve Quality of Experience and Quality of Service. Additionally, the deployment of additional servers to cover each user will ensure that user requirements are met even if the designated ES is unable to provide service. Therefore, the use of additional ESs can improve network robustness. However, edge service providers tend to cover all areas of a city with a minimum number of servers to save costs. Since the coverage zones of ESs can overlap, fewer additional ESs need to be deployed to support overlapping areas, resulting in cost savings. This article examines the problem of ES placement and proposes a new model to simultaneously optimize network latency, coverage with overlap control, and operational expenditures (OPEXs) of the MEC. In addition, a binary version of the hybrid NSGA II-MOPSO algorithm called BHNM is proposed to obtain the approximated Pareto front. Results based on the real-world data set from Shanghai Telecom show that the BHNM algorithm outperforms the binary MOPSO with turbulence (BMOPSO-T) and NSGA-II algorithms in terms of Pareto front diversity.

关键词： Edge server (ES) mobile edge computing (MEC) MOPSO multiobjective algorithm NSGA-II placement

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A Steady-State algorithm for Solving Expensive multiobjective Optimization Problems With Nonparallelizable Evaluations

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2023年第5期27卷 1544-1558页

作者： Rahi, Kamrul Hasan Singh, Hemant Kumar Ray, Tapabrata Univ New South Wales Sch Engn & Informat Technol Canberra ACT 2600 Australia

Expensive multiobjective optimization problems (EMOPs) refer to those wherein evaluation of each candidate solution incurs a significant cost. To solve such problems within a limited number of solution evaluations, surrogate-assisted evolutionary algorithms (SAEAs) are often used. However, existing SAEAs typically operate in a generational framework wherein multiple solutions are identified for evaluation in each generation. There exist relatively few proposals in steady-state framework, wherein only a single solution is evaluated in each iteration. The development of such algorithms is crucial to efficiently solve EMOPs for which the evaluation of candidate designs cannot be parallelized. Furthermore, regardless of the framework used, the performance of current SAEAs tends to degrade when the Pareto front (PF) of the problem has irregularities, such as extremely concave/convex segments, even for 2/3-objective problems. To contextualize the motivation of this study, the performance of a few state-of-the-art SAEAs is first demonstrated on some such selected problems. Then, to address the above research gaps, we propose a surrogate-assisted steady-state EA (SASSEA), which incorporates a number of novel elements, including: 1) effective use of model uncertainty information to aid the search, including the use of the probabilistic dominance and Mahalanobis distance;2) two-step infill identification using nondominance (ND) and distance-based selection;and 3) a shadow ND mechanism to avoid repeated selection and evaluation of dominated solutions. The efficacy of the proposed approach is demonstrated through extensive benchmarking on a range of test problems. It shows competitive performance relative to many state-of-the-art SAEAs, including both steady-state and generational approaches.

关键词： Infill strategy multiobjective algorithm steady-state algorithm surrogate-assisted optimization.

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A multiobjective Simulated Kalman Filter Optimization algorithm 4

A Multiobjective Simulated Kalman Filter Optimization Algori...

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4th IEEE International Conference on Applied System Invention (IEEE ICASI)

作者： Azwan, A. Razak, A. Jusof, M. F. M. Nasir, A. N. K. Ahmad, M. A. UMP Fac Elect & Elect Engn FKEE Pekan Pahang 26600 Malaysia

ISBN: (纸本)9781538643426

This paper presents a new multiobjective type optimization algorithm known as a multiobjective Optimization Simulated Kalman Filter (MOSKF). It is a further enhancement of a single-objective Simulated Kalman Filter (SKF) optimization algorithm. A synergy between SKF and Non-dominated Solution (NS) approach is introduced to formulate the multiobjective type algorithm. SKF is a random based optimization algorithm inspired from Kalman Filter theory. A Kalman gain is formulated following the prediction, measurement and estimation steps of the Kalman filter design. The Kalman gain is utilized to introduce a dynamic step size of a search agent in the SKF algorithm. A Non-dominated Solution (NS) approach is utilized in the formulation of the multiobjective strategy. Cost function value and diversity spacing parameters are taken into consideration in the strategy. Every single agent carries those two parameters in which will be used to compare with other solutions from other agents in order to determine its domination. A solution that has a lower cost function value and higher diversity spacing is considered as a solution that dominates other solutions and thus is ranked in a higher ranking. The algorithm is tested with various multiobjective benchmark functions and compared with Non-Dominated Sorting Genetic algorithm 2 (NSGA2) multiobjective algorithm. Result of the analysis on the accuracy tested on the benchmark functions is tabulated in a table form and shows that the proposed algorithm outperforms NSGA2 significantly. The result also is presented in a graphical form to compare the generated Pareto solution based on proposed MOSKF and original NSGA2 with the theoretical Pareto solution.

关键词： Simulated Kalman Filter Non-dominated Sorting multiobjective algorithm

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Expensive multiobjective Optimization Based on Information Transfer Surrogate

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2023年第3期53卷 1684-1696页

作者： Luo, Jianping Dong, YongFei Zhu, Zexuan Cao, Wenming Li, Xia Shenzhen Univ Coll Elect & Informat Engn Guangdong Key Lab Intelligent Informat Proc Shenzhen 518060 Peoples R China Shenzhen Univ Coll Comp & Software Engn Shenzhen 518060 Peoples R China Shenzhen Univ Coll Elect & Informat Engn Shenzhen 518060 Peoples R China

Objective value estimation based on computationally efficient surrogate models is widely used to reduce the computational cost in solving expensive multiobjective optimization problems (MOPs). However, due to the scarcity of training data and the lack of data sharing between training tasks in a surrogate-based system, the estimation effectiveness of the surrogate models might not be satisfactory. In this study, we present a novel surrogate methodology based on information transfer to deal with this problem. Particularly, in the proposed framework, the objectives of an MOP that may have little apparent similarity or correlation are linearly mapped to a number of related tasks. Afterward, the related tasks are used to train a multitask Gaussian process (MTGP). MTGP expands the training data leading to more confident learning of the parameters of the model. The predicted values of the objective functions can be obtained by a reverse mapping from the learned MTGP model. In this way, the computational burden of the expensive objective functions of an MOP can be substantially reduced while maintaining good estimation accuracy. MTGP facilitates mutual information transfer across tasks, avoids learning from scratch for new tasks, and captures the underlying structural information between tasks. The proposed surrogate approach is merged into MOEA/D to address MOPs. Experimental tests under various scenarios indicate that the resultant algorithm outperforms other state-of-the-art surrogate-based multiobjective optimization algorithms.

关键词： Optimization Task analysis Linear programming Computational modeling Correlation Estimation Data models Expensive optimization Gaussian process (GP) information transfer multiobjective algorithm optimization methods surrogate

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multiobjective Patient Stratification Using Evolutionary multiobjective Optimization

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2018年第5期22卷 1619-1629页

作者： Li, Xiangtao Wong, Ka-Chun Northeast Normal Univ Dept Comp Sci & Informat Technol Changchun 130117 Jilin Peoples R China City Univ Hong Kong Dept Comp Sci Kowloon Tong Hong Kong Peoples R China

One of the main challenges in modern medicine is to stratify patients for personalized care. Many different clustering methods have been proposed to solve the problem in both quantitative and biologically meaningful manners. However, existing clustering algorithms suffer from numerous restrictions such as experimental noises, high dimensionality, and poor interpretability. To overcome those limitations altogether, we propose and formulate a multiobjective framework based on evolutionary multiobjective optimization to balance the feature relevance and redundancy for patient stratification. To demonstrate the effectiveness of our proposed algorithms, we benchmark our algorithms across 55 synthetic datasets based on a real human transcription regulation network model, 35 real cancer gene expression datasets, and two case studies. Experimental results suggest that the proposed algorithms perform better than the recent state-of-the-arts. In addition, time complexity analysis, convergence analysis, and parameter analysis are conducted to demonstrate the robustness of the proposed methods from different perspectives. Finally, the t-Distributed Stochastic Neighbor Embedding (t-SNE) is applied to project the selected feature subsets onto two or three dimensions to visualize the high-dimensional patient stratification data.

关键词： Patient stratification multiobjective algorithm clustering

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A Composite Entropy Model in a multiobjective Framework for Gene Regulatory Networks

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CURRENT BIOINFORMATICS 2018年第1期13卷 85-94页

作者： Majumder, Aurpan Sarkar, Mrityunjay Dash, Harihar Akhilesh, Indupalli NIT Durgapur Fac Engn Dept ECE Durgapur 713209 India

Background: Transcription Factors (TFs) play a pivotal role in a Gene Regulatory Network (GRN) by differentially regulating genes across conditions. In some cases, it requires coordinated regulation of multiple TFs to control a Differentially Expressed (DE) gene. In this line, we have also developed simple architectures to unveil the parallel regulatory control by TFs. Objective: To date there are few works that have conducted active research to develop serial TF regulatory paths. In order to make some contribution in this specific area, here we have proposed an algorithm which puts up an architecture of multiple serial TF regulatory paths for a target gene. Methods: In order to explore the full potential of our algorithm we have tested it on one synthetic and three eukaryotic organism gene expression datasets. We were able to construct multiple transcription factor regulatory paths with varying lengths to each target differentially expressed gene with such transcription factors distributed across various multiobjective optimal fronts based on their regulatory properties. This is followed by multiple stage minimal entropy analysis. Conclusion: Through this multiple stage composite entropy approach we have not only assessed the strength of transcription factor to target interaction pathways supported by different literatures but added some new interactions and deleted a few existing ones having weak regulatory control probabilities.

关键词： Regulatory Impact Factor (RIF) Differential Correlation Set Analysis (dCSA) Partial Correlation with Information Theory (PCIT) multiobjective algorithm regulator target entropy steepest descent

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