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Multiobjective nondominated neighbor coevolutionary algorithm with elite population

SOFT COMPUTING 2015年第5期19卷 1329-1349页

作者： Mu, Caihong Jiao, Licheng Liu, Yi Li, Yangyang Xidian Univ Int Res Ctr Intelligent Percept & Computat Minist Educ Key Lab Intelligent Percept & Image Understanding Xian 710071 Shannxi Peoples R China Xidian Univ Sch Elect Engn Xian 710071 Peoples R China

A nondominated neighbor coevolutionary algorithm (NNCA) with a novel coevolutionary mechanism is proposed for multiobjective optimization, where elite individuals are used to guide the search. All the nondominated individuals are divided into two subpopulations, namely, the elite population and the common population according to their crowding-distance values. The elite individual located in less-crowded region will have more chances to select more team members for its own team and thus this region can be explored more sufficiently. Therefore, the elite population will guide the search to the more promising and less-crowded region. Secondly, to avoid the 'search stagnation' situation which means that algorithms fail to find enough nondominated solutions, a size guarantee mechanism (SGM) is proposed for elite population by emigrating some dominated individuals to the elite population when necessary. The SGM can prevent the algorithm from searching around limited nondominated individuals and being trapped into the 'search stagnation' situation. In addition, several different kinds of crossover and mutation operator are used to generate offspring, which are benefits for the diversity property. Tests on 20 multiobjective optimization benchmark problems including five ZDT problems, five DTLZ problems and ten unconstrained CEC09 test problems show that NNCA is very competitive compared with seven the state-of-the-art multiobjective optimization algorithms.

关键词： multiple objective programming Evolutionary computations Coevolutionary algorithm Elite population

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Network design approach for hub ports-shipping companies competition and cooperation

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TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE 2013年 48卷 1-18页

作者： Asgari, Nasrin Farahani, Reza Zanjirani Goh, Mark Univ Portsmouth Dept Math Logist & Management Math Grp Portsmouth PO1 2UP Hants England Univ Kingston Kingston Business Sch Dept Informat & Operat Management London England Univ S Australia Sch Management Adelaide SA 5001 Australia Natl Univ Singapore NUS Business Sch Singapore 117548 Singapore

In the maritime industry, the stakeholders comprising the port authorities, shipping companies, and port operators often compete and collaborate within an ecological system. This paper investigates the competition and cooperation strategies amongst three parties: two major container hub ports and the shipping companies. This research develops a game theoretic network design model which considers three scenarios: (i) perfect competition between the hub ports, (ii) perfect cooperation between the hub ports, and (iii) cooperation between the shipping companies and the hub ports as a whole. The scenarios are tested using empirical data from two leading Asian hub ports: Singapore and Hong Kong. An interval branch and bound is designed to solve the models. Crown Copyright (C) 2012 Published by Elsevier Ltd. All rights reserved.

关键词： Maritime transport Container port Network design multiple objective programming

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A multi-objective resource allocation problem in PERT networks

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2006年第3期172卷 838-854页

作者： Azaron, A Katagiri, H Sakawa, M Kato, K Memariani, A Hiroshima Univ Grad Sch Engn Dept Artificial Complex Syst Engn Higashihiroshima 7398527 Japan Bu Ali Sina Univ Dept Ind Engn Hamadan Iran

We develop a multi-objective model for resource allocation problem in PERT networks with exponentially or Erlang distributed activity durations, where the mean duration of each activity is a non-increasing function and the direct cost of each activity is a non-decreasing function of the amount of resource allocated to it. The decision variables of the model are the allocated resource quantities. The problem is formulated as a multi-objective optimal control problem that involves four conflicting objective functions. The objective functions are the total direct costs of the project (to be minimized), the mean of project completion time (min), the variance of project completion time (min), and the probability that the project completion time does not exceed a certain threshold (max). The surrogate worth trade-off method is used to solve a discrete-time approximation of the original problem. (c) 2005 Elsevier B.V. All rights reserved.

关键词： multiple objective programming project management and scheduling optimal control Markov processes

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Constrained multi-objective optimization of compact microwave circuits by design triangulation and pareto front interpolation

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2022年第1期299卷 302-312页

作者： Koziel, Slawomir Pietrenko-Dabrowska, Anna Reykjavik Univ Engn Optimizat & Modeling Ctr IS-102 Reykjavik Iceland Gdansk Univ Technol Fac Elect Telecommun & Informat PL-80233 Gdansk Poland

Development of microwave components is an inherently multi-objective task. This is especially pertinent to the design closure stage, i.e., final adjustment of geometry and/or material parameters carried out to improve the electrical performance of the system. The design goals are often conflicting so that the improvement of one normally leads to a degradation of others. Compact microwave passives constitute a representative case: reduction of the circuit footprint area is detrimental to electrical figures of merit (e.g., the operating bandwidth). Identification of the best available trade-off designs requires multiobjective optimization (MO). This is a computationally expensive task, especially when executed at the level of full-wave electromagnetic (EM) simulation. The computational complexity issue can be mitigated through the employment of surrogate modeling methods, yet their application is limited by a typically high nonlinearity of system responses, and the curse of dimensionality. In this paper, a novel technique for fast MO of compact microwave components is proposed, which allows for sequential rendition of the trade-off designs using triangulation of the already available Pareto front as well as rapid refinement algorithms. Our methodology is purely deterministic;in particular, it does not rely on population-based nature-inspired procedures. The three major benefits are low computational cost, possibility of handling explicit design constraints, and a capability of producing a visually uniform representation of the Pareto front. The algorithm is demonstrated using a compact branch-line coupler and a three-section impedance matching transformer. In both cases, considerable savings are obtained over the benchmark, here, the state-of-the-art surrogate-assisted MO technique.

关键词： multiple objective programming Microwave design Simulation -based design Triangulation Pareto front interpolation

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Discrete representation of the non-dominated set for multi-objective optimization problems using kernels

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2017年第3期260卷 814-827页

作者： Bazgan, Cristina Jamain, Florian Vanderpooten, Daniel Univ Paris 09 PSL Res Univ CNRS LAMSADE F-75016 Paris France Inst Univ France Paris France

In this paper, we are interested in producing discrete and tractable representations of the set of non dominated points for multi-objective optimization problems, both in the continuous and discrete cases. These representations must satisfy some conditions of coverage, i.e. providing a good approximation of the non-dominated set, spacing, i.e. without redundancies, and cardinality, i.e. with the smallest possible number of points. This leads us to introduce the new concept of (epsilon, epsilon')-kernels, or epsilon-kernels when epsilon' = epsilon is possible, which correspond to epsilon=Pareto sets satisfying an additional. condition, of epsilon'-stability. Among these, the kernels of small, or possibly optimal, cardinality are claimed to be good representations of the non-dominated set. We first establish some general properties on epsilon-kernels. Then, for the bi-objective case, we propose some generic algorithms computing in polynomial time either an epsilon-kernel of small size or, for a fixed size k, an epsilon-kernel with a nearly optimal approximation ratio 1 + epsilon. For more than two objectives, we show that epsilon-kernels do not necessarily exist but that (epsilon, epsilon' )-kernels with epsilon' <= root 1 + epsilon-1 always exist. Nevertheless, we show that the size of a smallest (epsilon, epsilon')-kernel can be very far from the size of a smallest epsilon-Pareto set. (C) 2016 Published by Elsevier B.V.

关键词： multiple objective programming Pareto set Non-dominated points Discrete representation Exact and approximation algorithms kernel

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Multi-objective solid transportation problems with budget constraint in uncertain environment

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2014年第8期45卷 1668-1682页

作者： Kundu, Pradip Kar, Samarjit Maiti, Manoranjan Natl Inst Technol Dept Math Durgapur India Vidyasagar Univ Dept Appl Math Oceanol & Comp Programming Midnapore India

This paper investigates multi-objective solid transportation problems (MOSTP) under various uncertain environments. The unit transportation penalties/costs are taken as random, fuzzy and hybrid variables respectively, in three different uncertain multi-objective solid transportation models and in each case, the supplies, demands and conveyance capacities are fuzzy. Also, apart from source, demand and capacity constraints, an extra constraint on the total budget at each destination is imposed. Chance-constrained programming technique has been used for the first two models to obtain crisp equivalent forms, whereas expected value model is formulated for the last. We provide an another approach using the interval approximation of fuzzy numbers for the first model to obtain its crisp form and compare numerically two approaches for this model. Fuzzy programming technique and a gradient based optimisation - generalised reduced gradient (GRG) method are applied to beget the optimal solutions. Three numerical examples are provided to illustrate the models and programming.

关键词： solid transportation problem multiple objective programming uncertain variable chance-constrained programming fuzzy programming technique

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Optimal water and land resource allocation in pastoral areas based on a water-land forage-livestock balance: a case study of Otog Front Banner, Inner Mongolia, China

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2020年第10期27卷 10328-10341页

作者： Lu, Haiyuan Li, Heping Wang, Jun Zheng, Hexiang Cao, Xuesong Tong, Changfu China Inst Water Resources & Hydropower Res State Key Lab Simulat & Regulat Water Cycle River Beijing 10038 Peoples R China Minist Water Resources Peoples Republ China Inst Water Resources Pastoral Area Hohhot 010020 Peoples R China

Natural grasslands provide important land resources in pastoral areas, and greatly contribute to ecological functioning. Overgrazing and other unreasonable exploitations have led to the degradation and desertification of natural grasslands, exacerbating the forage-livestock imbalance. In areas suffering from water shortage, this imbalance gradually evolves into a water-land forage-livestock imbalance. In this study, a water-land forage-livestock balance-based model was developed to optimise the allocation of water, land, and forage resources in pastoral areas, while addressing economic and ecological benefits in a coupled manner. The model was applied in a case study of Otog Front Banner to simulate the comprehensive economic and ecological benefits to the development of water, land, and forage resources in different coupled allocations of artificial and natural grasslands. The results showed that as the duration of supplementary and barn feeding increased, local development was first constrained by the availability of natural grasslands and then by the availability of water resources. The optimal resource allocation in Otog Front Banner predicted for 2030 included a water consumption of 266,000,000 m(3), an irrigation area of 43,000 ha, a natural grassland utilisation area of 684,700 ha, and a livestock farming scale of 1,188,500 sheep units.

关键词： Water and land resources Forage resources Optimal allocation Water-land forage-livestock balance multiple objective programming Pastoral area

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An iterative approach to solve multiobjective linear fractional programming problems

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APPLIED MATHEMATICAL MODELLING 2014年第1期38卷 38-49页

作者： Valipour, E. Yaghoobi, M. A. Mashinchi, M. Shahid Bahonar Univ Kerman Fac Math & Comp Dept Math Kerman Iran Shahid Bahonar Univ Kerman Fac Math & Comp Dept Stat Kerman Iran

This paper suggests an iterative parametric approach for solving multiobjective linear fractional programming (MOLFP) problems which only uses linear programming to obtain efficient solutions and always converges to an efficient solution. A numerical example shows that this approach performs better than some existing algorithms. Randomly generated MOLFP problems are also solved to demonstrate the performance of new introduced algorithm. (C) 2013 Elsevier Inc. All rights reserved.

关键词： multiple objective programming Fractional programming Efficiency Iterative methods Linear programming

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A goal programming approach to solve the multiple criteria DEA model

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2017年第1期260卷 134-139页

作者： dos Santos Rubem, Ana Paula Soares de Mello, Joao Carlos C. B. Meza, Lidia Angulo Ctr Naval Syst Anal Decis Aid Dept Praca Barao Ladarias-nIlha Cobras BR-20091000 Rio De Janeiro RJ Brazil Univ Fed Fluminense Dept Prod Engn Rua Passo Patria 156Sao Domingos BR-24210240 Niteroi RJ Brazil

The lack of discrimination power and the inappropriate multipliers schemes remain major issues in data envelopment analysis (DEA). To overcome these problems, the multiple criteria DEA (MCDEA) model was introduced in the late 1990s, drawing from a multiple objective perspective. However, because the objectives of the MCDEA model are generally conflicting, an optimal solution satisfying all objectives simultaneously often does not exist. Within this context, goal programming (GP) approaches were proposed to solve the MCDEA model. This paper focuses specifically on the GP formulation, known as GPDEA. However, recently, the GPDEA models were found to be invalid, and no alternative formulation, under a GP framework, was proposed. Therefore, the aim here is to develop a formulation for adequately solving the MCDEA model using weighted GP. In order to do so, we point out inconsistencies in the existing GPDEA models, and we present the WGP-MCDEA model. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Data envelopment analysis multiple criteria analysis Goal programming multiple objective programming

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Approximating the Pareto optimal set using a reduced set of objective functions

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2010年第3期207卷 1519-1534页

作者： Lindroth, Peter Patriksson, Michael Stromberg, Ann-Brith Univ Gothenburg SE-41296 Gothenburg Sweden Chalmers Univ Technol SE-41296 Gothenburg Sweden

Real-world applications of multi-objective optimization often involve numerous objective functions. But while such problems are in general computationally intractable, it is seldom necessary to determine the Pareto optimal set exactly. A significantly smaller computational burden thus motivates the loss of precision lithe size of the loss can be estimated. We describe a method for finding an optimal reduction of the set of objectives yielding a smaller problem whose Pareto optimal set w.r.t. a discrete subset of the decision space is as close as possible to that of the original set of objectives. Utilizing a new characterization of Pareto optimality and presuming a Finite decision space, we derive a program whose solution represents an optimal reduction. We also propose an approximate, computationally less demanding formulation which utilizes correlations between the objectives and separates into two parts. Numerical results from an industrial instance concerning the configuration of heavy-duty trucks are also reported. demonstrating the usefulness of the method developed. The results show that multi-objective optimization problems can be significantly simplified with an induced error which can be measured. (C) 2010 Elsevier B.V. All rights reserved.

关键词： multiple objective programming Many-objective optimization Pareto optimality objective space reduction Engineering optimization

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