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A multi-objective Optimization Approach for Water-Energy-Food Grids in Isolated Communities

PROCESS INTEGRATION AND OPTIMIZATION FOR SUSTAINABILITY 2019年第4期3卷 471-485页

作者： Sanchez-Zarco, Xate Geraldine Cansino-Loeza, Brenda Ponce-Ortega, Jose Maria Univ Michoacana Chem Engn Dept Francisco J Mujica S-NCiudad Univ Morelia 58060 Michoacan Mexico

This paper evaluates the applicability of different multi-objective optimization methods such as goal programming, weighted sum, and epsilon constraint in a polygeneration system. The problem under study aims to meet the water, energy, and food demands in an isolated community. The model includes three fundamental objectives, which are the maximization of the economic benefit, the minimization of the greenhouse gas emissions, and the minimization of freshwater consumption. Because the objectives are in conflict, it is necessary to implement strategies that allow to obtain trade-off solutions. To show the applicability of the proposed approach, a case study for the community of Mexico with the lowest human development index is presented. The results show feasible solutions for satisfying the needs of the community considering the trade-offs of the different objectives, with the goal programming method the one that provides the most attractive solution except for the economic objective, unlike the Epsilon Constraint method that provides the best solutions with respect to the economic objective.

关键词： Optimization Process integration Isolated communities Simulation multi-objective problems

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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 Novel Pareto Archive Evolution Algorithm with Adaptive Grid Strategy for multi-objective Optimization Problem 23

A Novel Pareto Archive Evolution Algorithm with Adaptive Gri...

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IEEE 23rd International Conference on Computer Supported Cooperative Work in Design (CSCWD)

作者： Zhao, Fuqing He, Xuan Zhang, Yi Ma, Weimin Zhang, Chuck Lanzhou Univ Technol Sch Comp & Commun Technol Lanzhou 730050 Gansu Peoples R China Xijin Univ Sch Mechn Engn Xian 710123 Shaanxi Peoples R China Tongji Univ Sch Econ & Management Shanghai 200092 Peoples R China Georgia Inst Technol H Milton Stewart Sch Ind & Syst Engn Atlanta GA 30332 USA

ISBN: (纸本)9781728103501

multi-objective evolutionary algorithms usually utilize fixed evolutionary mechanism and the evolutionary operators are static during the process of algorithm evolution. It is easy to cause a simple population structure, unable to exploit the search space fully and trapped in local optimal solution. In this paper, a novel method named Pareto Archive Evolution Strategy (PAES) with adaptive grid strategy (AGS_PAES) which only makes one mutation to create one new solution and use an "archive" which are called Non-Dominated Archive to store the best solution, is introduced. This procedure is completed by a special approach - adaptive grid method, which decides the criterion of the solution to be archived and the place of the grid location the solution would be stored. The Pareto front obtained by the procedure outperforms the classical multi-objective Genetic Algorithm (MOGA). Simulation results on the standard benchmark problems show that the proposed adaptive scheme has a better convergence and diversity compared with the second generation classical multi -objective evolutionary algorithms.

关键词： multi-objective problems Pareto Archived Evolution Strategy Non-Dominated Archive adaptive grid strategy Pareto front

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Images, Fixed Points and Vector Extremum problems

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JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS 2018年第3期177卷 889-905页

作者： Antoni, Carla Alshahrani, Mohammed Naval Acad Livorno Italy King Fahd Univ Petr & Minerals Dhahran Saudi Arabia

Based on recent results on image space analysis, the paper aims at describing a fixed point approach to vector optimization problems. Possible extensions to the bi-level vector optimization are discussed.

关键词： Image apace analysis Vector optimization problems multi-objective problems Existence of maxima and minima Bi-level optimization Vector variational inequalities

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Enhanced multi-objective particle swarm optimisation postures

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KNOWLEDGE-BASED SYSTEMS 2018年 158卷 175-195页

作者： Saremi, Shahrzad Mirjalili, Seyedali Lewis, Andrew Liew, Alan Wee Chung Dong, Jin Song Griffith Univ Inst Integrated & Intelligent Syst Brisbane Qld 4111 Australia

multi-objective problems with conflicting objectives cannot be effectively solved by aggregation-based methods. The answer to such problems is a Pareto optimal solution set. Due to the difficulty of solving multi-objective problems using multi-objective algorithms and the lack of enough expertise, researchers in different fields tend to aggregative objectives and use single-objective algorithms. This work is a seminal attempt to propose the use of multi-objective algorithms in the field of hand posture estimation. Hand posture estimation is a key step in hand gesture recognition, which is a part of an overall attempt to make human-computer interaction more like human face-to-face communication. Hand posture estimation is first formulated as a bi-objective problem. A modified version of multi-objective Particle Swarm Optimisation (MOPSO) is then proposed to approximate the Pareto optimal font of 50 different postures. The main motivation of integrating a new operator (called Evolutionary Population Dynamics EPD) in MOPSO is due to the nature of hand posture estimation problems in which parameters should not be tuned in a same manner since they show varied impacts on the objectives. EPD allows randomising different parameters in a solution and provides different exploratory behaviours for the parameters of an optimisation algorithm rather than each individual solution. The MOPSO algorithm is equipped with a mechanism to randomly re-initialise poor particles around the optimal solutions in the archive. The improved MOPSO is tested on ZDT and CEC2009 test functions and compared with the standard MOPSO, NSGA-II, and MOEA/D. The results show that the proposed MOPSO (MOPSO + EPD) significantly outperforms MOPSO on the majority of test functions in terms of both convergence and coverage. MOPSO + EPD also approximates well-distributed Pareto optimal fronts for most of the postures considered in this work. The post analysis of the results is conducted to understand the relati

关键词： multi-objective problems multi-objective optimisation multi-objective particle swarm optimisation MOPSO Hand posture estimation Particle Swarm Optimization Heuristic algorithm Metaheuristics Constrained optimization Benchmark

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Optimisation of engineering system using a novel search algorithm: the Spacing multi-objective Genetic Algorithm

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CONNECTION SCIENCE 2018年第3期30卷 326-342页

作者： Falahiazar, L. Shah-Hosseini, H. Islamic Azad Univ Dept Comp Engn Sci & Res Branch Tehran Iran

A large number of real-world issues are among difficult and multi-objective problems. Recently, it has been recognised that the evolutionary algorithms optimise well these types of problems. This paper proposes a novel multi-objective search algorithm that is called the Spacing multi-objective Genetic Algorithm (Spacing-MOGA). The innovation of the proposed Spacing-MOGA lies in a new survival selection algorithm called Spacing Distance. This research eliminates some of the disadvantages of other algorithms such as the Non-dominated Sorting Genetic Algorithm II (NSGAII). The proposed Spacing-MOGA is applied to five test benchmark functions and also to the design of I-Beam. Then, the results are compared with other algorithms such as NSGAII, Adaptive Weighted Particle Swarm Optimisation (AWPSO), and Non-dominated Sorting Particle Swarm Optimiser (NSPSO) based on the test metrics: Hypervolume, Spacing, Spread, and Generational Distance. Furthermore, for further demonstration of the ability of the proposed Spacing-MOGA, the experimental results are evaluated by the t-test.

关键词： multi-objective problems Non-dominated Sorting Genetic Algorithm II Non-dominated Sorting Particle Swarm Optimiser Adaptive Weighted Particle Swarm Optimisation I-Beam

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Images and Existence of Constrained Scalar and Vector Extrema

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JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS 2018年第3期177卷 865-888页

作者： Antoni, Carla Giannessi, Franco Naval Acad Sect Math Livorno Italy Univ Pisa Dept Math Pisa Italy

By means of the image space analysis, a necessary and sufficient condition is established for the existence of vector extrema. In the scalar case, the sufficient part is shown to shrink to a known one.

关键词： Image space analysis Vector optimization problems multi-objective problems Existence of maxima and minima Bi-level optimization

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An efficient bi-objective algorithm to solve re-entrant hybrid flow shop scheduling with learning effect and setup times

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OPERATIONAL RESEARCH 2018年第1期18卷 123-158页

作者： Mousavi, S. M. Mahdavi, I. Rezaeian, J. Zandieh, M. Mazandaran Univ Sci & Technol Dept Ind Engn Babol Sar Iran Shahid Beheshti Univ Dept Ind Management Management & Accounting Fac GC Tehran Iran

This paper deals with a bi-objective hybrid flow shop scheduling problem minimizing the maximum completion time (makespan) and total tardiness, in which we consider re-entrant lines, setup times and position-dependent learning effects. The solution method based on genetic algorithm is proposed to solve the problem approximately, which belongs to non-deterministic polynomial-time (NP)-hard class. The solution procedure is categorized through methods where various solutions are found and then, the decision-makers select the most adequate (a posteriori approach). Taguchi method is applied to set the parameters of proposed algorithm. To demonstrate the validation of proposed algorithm, the full enumeration algorithm is used to find the Pareto-optimal front for special small problems. To show the efficiency and effectiveness of the proposed algorithm in comparison with other efficient algorithm in the literature (namely MLPGA) on our problem, the experiments were conducted on three dimensions of problems: small, medium and large. Computational results are expressed in terms of standard multi-objective metrics. The results show that the proposed algorithm is able to obtain more diversified and competitive Pareto sets than the MLPGA.

关键词： Re-entrant hybrid flow shop Setup times Learning effect multi-objective problems A posteriori approach

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Some Perspectives on Vector Optimization via Image Space Analysis

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JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS 2018年第3期177卷 906-912页

作者： Giannessi, Franco Univ Pisa Dept Math Pisa Italy

This note aims at stimulating research on vector optimization through Image Space Analysis.

关键词： Image Space Analysis Vector optimization problems multi-objective problems Existence of maxima and minima Bi-level optimization Vector Variational Inequalities

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Some Perspectives on Set-Valued Optimization via Image Space Analysis

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JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS 2018年第3期177卷 811-815页

作者： Pellegrini, Letizia Univ Verona Dept Econ Via Cantarane 24 I-37129 Verona Italy

This note aims at stimulating research on set-valued optimization through image space analysis.

关键词： Image space analysis Vector optimization multi-objective problems Set-valued optimization problems

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