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multi-objective optimal structural design of composite superstructure using a novel MONMPSO algorithm

INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES 2021年 193卷

作者： Gholami, Meghdad Fathi, Alireza Baghestani, Ali Mohammad Babol Noshirvani Univ Technol Dept Mech Engn Babol Iran

As an application of composite science in the marine industry, the present paper deals with the multi-objective optimal structural design of a superstructure composite sandwich panel based on the first-order shear deformation laminated plate theory (FSDT). Several parameters including the type of fiber, matrix and core material, the amount of reinforcement, the core, lamina and laminate thickness, the laminate arrangement (stacking sequence) and the laminate construction are considered as the design parameters. A novel multi-objective Niching Memetic Particle Swarm optimization (MONMPSO) algorithm is proposed and its performance is evaluated using the well-known non-dominated sorting genetic algorithm (NSGA-II). The results show that the proposed MONMPSO algorithm has a better performance in comparison to the NSGA-II algorithm in extracting the Pareto front pattern. Based on the numerical results, many useful structural rules for designing a composite sandwich panel under the out of plane pressure and buckling load have been deduced.

关键词： Composite sandwich panel Fiber-reinforced laminate constrained multi-objective optimization MONMPSO NSGA-II FSDT

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Trading off costs and service rates in a first-mile ride-sharing service

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2023年 150卷

作者： Zheng, Minyi Pantuso, Giovanni Univ Copenhagen Dept Math Sci DK-2100 Copenhagen Denmark

Given a set of geographically dispersed vehicles, the first-mile ride-sharing problem seeks optimal routes to transport customers to a common destination (e.g., a transit station) via shared trips. In this article, we address the trade off between operating costs and service rates. The resulting multi-objective optimization problem is of a combinatorial nature. To obtain Pareto solutions we propose an evolutionary algorithm based on efficient non-dominated solution sorting. Extensive tests based on real-life data illustrate that the algorithm is able to find high-quality non-dominated fronts.

关键词： constrained multi-objective optimization problem First-mile ride-sharing problem Evolutionary algorithm

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A constrained multi-objective evolutionary algorithm with Pareto estimation via neural network

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 237卷

作者： Liu, Zongli Zhao, Peng Cao, Jie Zhang, Jianlin Chen, Zuohan Lanzhou Univ Technol Sch Comp & Commun Lanzhou 730050 Peoples R China Lanzhou Univ Technol Gansu Engn Res Ctr Mfg Informat Lanzhou 730050 Peoples R China

The main challenge in addressing constrained multi-objective optimization problems (CMOPs) lies in achieving a balance among convergence, diversity, and feasibility. To address this issue, this paper proposes a constrained multi-objective evolutionary algorithm with Pareto estimation via neural network named CMOEA-PeNN. In order to exploit and explore the decision space, the proposed algorithm employs a dual-population mechanism, which is trained with a self-organizing map (SOM). Firstly, the population distribution structure in decision space is mapped to objective space while preserving neighborhood information, and then the neuron weight is utilized to estimate the Pareto front (PF). Secondly, a novel approach is devised to preserve the feasibility of the population and enhance the estimation of the Pareto front by SOM. The achievement scalarizing function (ASF) is employed to choose promising solutions. This strategy could guide the population toward the optimal solution while exploring the small feasible regions. Finally, the performance of CMOEA-PeNN is compared with five stateof-the-art constrained multi-objective evolutionary algorithms (CMOEAs) on three widely used benchmark problems and a real-world problem. The experimental results show that CMOEA-PeNN could archive competitive performance in solving CMOPs.

关键词： constrained multi-objective optimization Neural network Self-organizing map Pareto estimation Feasibility

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A pareto fronts relationship identification-based two-stage constrained evolutionary algorithm

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APPLIED SOFT COMPUTING 2024年 159卷

作者： Zhao, Kaiwen Tong, Xiangrong Wang, Peng Wang, Yingjie Chen, Yue Yantai Univ Sch Comp & Control Engn Yantai 264005 Peoples R China

Striking a balance between diverse constraints and conflicting objectives is one of the most crucial issues in solving constrained multi -objective optimization problems (CMOPs). However, it remains challenging to existing methods, due to the reduced search space caused by the constraints. For this issue, this paper proposes a Pareto fronts relationship identification -based two -stage constrained evolutionary algorithm called RITEA, which balances objective optimization and constraint satisfaction by identifying and utilizing the relationship between the unconstrained Pareto front (UPF) and the constrained Pareto front (CPF). Specifically, the evolutionary process is divided into two collaborative stages: training stage and reinforcement stage. In the training stage, a relationship identification method is developed to estimate the relationship between UPF and CPF, which guides the population search direction. In the reinforcement stage, the corresponding evolutionary strategies are designed based on the identified relationship to enhance the accurate search on the CPF. Furthermore, a dynamic preference fitness function (termed DPF ) is designed to adaptively maintain the balance of search preference between convergence and diversity. Compared to seven state-of-the-art algorithms on 36 benchmark CMOPs in three popular test suites, RITEA obtains 77.8% of the best IGD values and 66.7% of the best HV values. The experimental results show that RITEA exhibits highly competitively when dealing with CMOPs.

关键词： constrained multi-objective optimization Evolutionary algorithm Pareto fronts relationship identification Two-stage Dynamic preference fitness function

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A constrained multi-objective evolutionary algorithm based on decomposition with improved constrained dominance principle

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SWARM AND EVOLUTIONARY COMPUTATION 2022年 75卷

作者： Gu, Qinghua Bai, Jiaming Li, Xuexian Xiong, Naixue Lu, Caiwu Xian Univ Architecture & Technol Sch Resources Engn Xian 710055 Shaanxi Peoples R China Xian Univ Architecture & Technol Sch Management Xian Shaanxi Peoples R China Xian Univ Architecture & Technol Xian Key Lab Intelligent Ind Percept Calculat & De Xian 710055 Peoples R China Northeastern State Univ Dept Math & Comp Sci Tahlequah OK USA

There are various constraints in many real engineering problems, and the constrained handling methods play a key role in the constrained multi-objective evolutionary algorithms (CMOEAs). Many constraint-handling stra-tegies will struggle to reach optimum results when dealing with constrained multi-objective optimization problems (CMOPs) with large and complex infeasible areas. It brings stiffer challenges to CMOEAs in maintaining the convergence, diversity, and feasibility of the population. To remedy these issues, this paper proposes an improved constrained dominance principle (ICDP) embedded in MOEA/D, named MOEA/D-ICDP. In MOEA/D-ICDP, the original weight vector that a solution matches may not be the best suitable weight vector that this solution corresponds to, then there is a deviation between them. According to the above situation, ICDP is designed in this way. Firstly, a dynamic tolerability value about the deviation is introduced. Then ICDP adjusts the dominance relationship of solutions in light of the relationship between the deviation and tolerability so that it can preserve some valuable infeasible solutions in the early evolutionary stage and help the population to cross the large and complex regions. Three test suites and two real-world engineering optimization problems are used to evaluate the performance of the proposed MOEA/D-ICDP and the other five representative CMOEAs. The experimental results demonstrate that MOEA/D-ICDP has more excellent performance and competitiveness than the other five CMOEAs.

关键词： constrained multi-objective optimization MOEA D constrained dominance principle Improved constrained dominance principle

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Parallel predictive entropy search for multi-objective Bayesian optimization with constraints applied to the tuning of machine learning algorithms

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EXPERT SYSTEMS WITH APPLICATIONS 2023年 215卷

作者： Garrido-Merchan, Eduardo C. Fernandez-Sanchez, Daniel Hernandez-Lobato, Daniel Univ Pontificia Comillas Quantitat Methods Dept Madrid Spain Univ Autonoma Madrid Comp Sci Dept Madrid Spain

Real-world problems often involve the optimization of several objectives under multiple constraints. An example is the hyper-parameter tuning problem of machine learning algorithms. For example, minimizing both an estimate of the generalization error of a deep neural network and its prediction time. We may also consider, as a constraint, that the deep neural network must be implemented in a chip with an area below some size. Here, both the objectives and the constraint are black boxes, i.e., functions whose analytical expressions are unknown and are expensive to evaluate. Bayesian optimization (BO) methods have shown state-of-the-art results in these tasks. For this, they evaluate iteratively, at carefully chosen locations, the objectives and the constraints with the goal of solving the optimization problem in a small number of iterations. Nevertheless, most BO methods are sequential and perform evaluations at just one input location, at each iteration. Sometimes, however, we may evaluate several configurations in parallel. If this is the case, as when a cluster of computers is available, sequential evaluations result in a waste of resources. To avoid this, one has to choose which locations to evaluate in parallel, at each iteration. This article introduces PPESMOC, Parallel Predictive Entropy Search for multi-objective Bayesian optimization with Constraints, an information -based batch method for the simultaneous optimization of multiple expensive-to-evaluate black-box functions under the presence of several constraints. Iteratively, PPESMOC selects a batch of input locations at which to evaluate the black-boxes in parallel so as to maximally reduce the entropy of the Pareto set of the optimization problem. To our knowledge, this is the first information-based batch method for constrained multi-objective BO. We present empirical evidence in the form of several optimization problems that illustrate the effectiveness of PPESMOC. Moreover, we also show in several expe

关键词： Bayesian optimization Gaussian processes Batch optimization constrained multi-objective optimization

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A constrained multi-objective evolutionary algorithm based on decomposition and dynamic constraint-handling mechanism

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APPLIED SOFT COMPUTING 2020年 89卷

作者： Yang, Yongkuan Liu, Jianchang Tan, Shubin Northeastern Univ Dept Informat Sci & Engn Shenyang 110819 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China

constrained multi-objective optimization problems (CMOPs) are common in real-world engineering application, and are difficult to solve because of the conflicting nature of the objectives and many constraints. Some constrained multi-objective evolutionary algorithms (CMOEAs) have been developed for CMOPs, but they still suffer from the problems of easily getting trapped into local optimal solutions and low convergence. This paper introduces a multi-objective evolutionary algorithm based on decomposition and dynamic constraint-handling mechanism (MOEA/D-DCH) to tackle this issue. Firstly, the dynamic constraint-handling mechanism divides the search modes into the unconstrained search mode and the constrained search mode, which are dynamically adjusted by the generation number and the proportion of feasible solutions in the population. This mechanism could lead to a faster convergence than the traditional constraint-handling mechanisms. For the constrained search mode, an improved epsilon constraint-handling method is used to enhance the diversity of the population. Then, an individual update mechanism based on the best feasible solution of each sub-problem is designed to update the feasible individuals for maintaining the convergence of the feasible solutions. Finally, MOEA/D-DCH dynamically regulates the parameters of the differential evolution operator to enhance the local search ability. Experiments on 21 benchmark test functions are conducted to test MOEA/D-DCH and five other typical CMOEAs. Meanwhile, a real-world problem is employed to evaluate the practical performance of MOEA/D-DCH. MOEA/D-DCH achieves significantly better results than the other five algorithms on most of the test problems. The results indicate the effectiveness and competitiveness of MOEA/D-DCH for solving CMOPs. (C) 2020 Elsevier B.V. All rights reserved.

关键词： constrained multi-objective optimization MOEA/D Constraint-handling techniques Epsilon constraint-handling Differential evolution

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A coevolutionary constrained multi-objective algorithm with a learning constraint boundary

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APPLIED SOFT COMPUTING 2023年 148卷

作者： Cao, Jie Yan, Zesen Chen, Zuohan Zhang, Jianlin Lanzhou Univ Technol Sch Comp & Commun Technol Lanzhou 730050 Peoples R China Lanzhou Univ Technol Gansu Engn Res Ctr Mfg Informat Lanzhou 730050 Peoples R China

When solving constrained multi-objective optimization problems, the balance of convergence, diversity, and feasibility plays a pivotal role. To address this issue, this paper proposes a coevolutionary constrained multi-objective algorithm with learning constraint boundary (CCMOLCB). Firstly, the constrained multi-objective problems are transformed by adding an additional objective using the constraint violation degree. Then, the transformed problem is solved by an improved coevolutionary framework which employs two populations. The main population explores the objective space and repairs infeasible solutions to maintain the feasibility of population. Meanwhile, the feasibility and diversity of solutions are balanced by using a dynamic weight coefficient during the evolution, it changes as the number of iterations increases. The subordinate population selects solutions by taking into consideration the learning constraint boundary (LCB). This boundary guarantees convergence of solutions by constraining the search range of the main population, thereby enhancing the environmental selection pressure. The performance of CCMOLCB is compared with seven state-of-the-art constrained multi-objective evolutionary algorithms on five test suites. The experimental results illustrate that CCMOLCB exhibits competitive performance when dealing with this family of problems. (c) 2023 Elsevier B.V. All rights reserved.

关键词： multiple-populations constrained multi-objective optimization constrained multi-objective evolutionary algorithms

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constrained multi-objective Differential Evolutionary Algorithm with Adaptive Constraint Handling Technique

World Scientific Research Journal

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World Scientific Research Journal 2021年第3期7卷 322-339页

作者： Caixia Cui Qinqin Fan

Finding feasible solutions and a good approximate Pareto front (PF) are two important tasks in the constrained multi-objective optimization (CMO). Various constraint handling techniques (CHTs) have a significant impact on these two tasks. To realize the adaptive adjustment of CHTs, a constrained multi-objective differential evolution algorithm with adaptive constraint handling technique (ACHT-CMODE) is proposed in the current study. In the ACHT-CMODE, three state-of-the-art constraint handling methods are integrated and an improved reverse generation distance is used to evaluate their performances. Also, the Q-learning method is utilized to guide the evolution of CHTs. The performance of the ACHT-CMODE is compared with that of the other five constraint multi-objective evolutionary algorithms on 18 test functions. Experimental results show that the overall performance of the ACHT-CMODE is the best among all compared algorithms, and the proposed algorithm is capable of selecting a suitable constraint handling method to solve a particular type of constrained multi-objective optimization problems (CMOPs).

关键词： constrained multi-objective optimization Constraint handling technique Adaptive Q-learning

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Emergency Scheduling optimization Based on Improved Artificial Bee Colony Algorithm

Emergency Scheduling Optimization Based on Improved Artifici...

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2015 6th IEEE International Conference on Software Engineering and Service Science(ICSESS 2015)

作者： ZHAO Ming SONG Xiao-Yu Gao Yi-Chen School of Information & Control Engineering Shenyang Jianzhu University Information Technology Management Office Shenyang Gas Group Co.Ltd

When large scale disaster occurs suddenly, allocating scheme of the first batch of "life" emergency supplies directly determines rescue effect. To make lack loss evaluation of the supplies accordant with actual cases, this paper proposed nonlinear loss evaluation function, based on which constructed multi to multi constrained scheduling model with multi objectives. To improve the performance of artificial bee colony algorithm, this paper proposed following schemes: food sources initialization based on converse food source. reverse learning and comprehensive learning added into bee searching procedure. Experiments results on random data of three scales show that non dominated front solutions set solved by the improved algorithm is more diverse, wide spread and uniform.

关键词： nonlinear loss emergency scheduling model constrained multi-objective optimization artificial bee colony algorithm reverse learning comprehensive learning

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