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2010 IEEE World Congress on Computational Intelligence

作者： Willick, Kyle Wesolkowski, Slawomir Mazurek, Michael Canadian Forces Aerosp Warfare Ctr OR Team Ottawa ON Canada DRDC CORA Ottawa ON Canada

ISBN: (纸本)9781424481262

We apply the non-dominated sorting genetic algorithm-II (NSGA-II) to a multi-objective fleet-mix problem for risk mitigation. The Stochastic Fleet Estimation (SaFE) model, a Monte Carlo-based model, is used to determine average annual requirements which a fleet must meet. We search for Pareto-optimal combinations of platform-to-task assignments that can be used to complete SaFE generated scenarios. Solutions are evaluated using three objectives, with a goal of minimizing fleet cost, total task duration, and the risk that a solution will not be able to accomplish future scenarios. Optimization over all three objectives allowed for exploration of configurations which were low cost and low risk, a region not explored by prior experiments without the risk objective.

关键词： Monte Carlo based model Monte Carlo methods Pareto optimal combination Pareto optimisation evolutionary computation fleet mix problem genetic algorithms multiobjective evolutionary algorithm nondominated sorting genetic algorithm-II platform to task assignment risk analysis risk minimization stochastic fleet estimation stochastic processes transportation

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An efficient solution strategy for bilevel multiobjective optimization problems using multiobjective evolutionary algorithm

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SOFT COMPUTING 2021年第13期25卷 8241-8261页

作者： Li, Hong Zhang, Li Xidian Univ Sch Math & Stat Xian 710071 Peoples R China

An efficient solution strategy is proposed for bilevel multiobjective optimization problem (BLMOP) with multiple objectives at both levels when multiobjective optimization problem (MOP) at the lower level satisfies the convexity and differentiability for the lower-level decision variables. In the proposed strategy, the MOP at the lower level is first converted into a single-objective optimization formulation through adopting adaptive weighted sum scalarization, in which the lower-level weight vector is adjusted adaptively while the iteration progressing. The Karush-Kuhn-Tucker optimality conditions are used to the lower-level single-objective scalarization problem, thus the original BLMOP can be converted into a single-level MOP with complementarity constraints. Then an effective smoothing technique is suggested to cope with the complementarity constraints. In such a way, the BLMOP is finally formalized as a single-level constrained nonlinear MOP. A decomposition-based constrained multiobjective differential evolution is developed to solve this transformed MOP and some instances are tested to illustrate the feasibility and effectiveness of the solution methodology. The experimental results show that the proposed solution method possesses favorite convergence and diversity.

关键词： Bilevel multiobjective optimization Pareto optimality multiobjective evolutionary algorithm Weighted sum scalarization Smoothing technique

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A multiobjective evolutionary algorithm Based on Structural and Attribute Similarities for Community Detection in Attributed Networks

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IEEE TRANSACTIONS ON CYBERNETICS 2018年第7期48卷 1963-1976页

作者： Li, Zhangtao Liu, Jing Wu, Kai Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ Xian 710071 Shaanxi Peoples R China

Most of the existing community detection algorithms are based on vertex connectivity. While in many real networks, each vertex usually has one or more attributes describing its properties which are often homogeneous in a cluster. Such networks can be modeled as attributed graphs, whose attributes sometimes are equally important to topological structure in graph clustering. One important challenge is to detect communities considering both topological structure and vertex properties simultaneously. To this propose, a multiobjective evolutionary algorithm based on structural and attribute similarities (MOEA-SA) is first proposed to solve the attributed graph clustering problems in this paper. In MOEA-SA, a new objective named as attribute similarity SA is proposed and another objective employed is the modularity Q. A hybrid representation is used and a neighborhood correction strategy is designed to repair the wrongly assigned genes through making balance between structural and attribute information. Moreover, an effective multi-individual-based mutation operator is designed to guide the evolution toward the good direction. The performance of MOEA-SA is validated on several real Facebook attributed graphs and several ego-networks with multiattribute. Two measurements, namely density T and entropy E, are used to evaluate the quality of communities obtained. Experimental results demonstrate the effectiveness of MOEA-SA and the systematic comparisons with existing methods show that MOEA-SA can get better values of T and E in each graph and find more relevant communities with practical meanings. Knee points corresponding to the best compromise solutions are calculated to guide decision makers to make convenient choices.

关键词： Attribute similarity attributed networks community detection multiobjective evolutionary algorithm multiobjective optimization topological structure

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NMEEF-SD: Non-dominated multiobjective evolutionary algorithm for Extracting Fuzzy Rules in Subgroup Discovery

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2010年第5期18卷 958-970页

作者： Jose Carmona, Cristobal Gonzalez, Pedro Jose del Jesus, Maria Herrera, Francisco Univ Jaen Dept Comp Sci Jaen 23071 Spain Univ Granada Dept Comp Sci & Artificial Intelligence E-18071 Granada Spain

A non-dominated multiobjective evolutionary algorithm for extracting fuzzy rules in subgroup discovery (NMEEF-SD) is described and analyzed in this paper. This algorithm, which is based on the hybridization between fuzzy logic and genetic algorithms, deals with subgroup-discovery problems in order to extract novel and interpretable fuzzy rules of interest, and the evolutionary fuzzy system NMEEF-SD is based on the well-known Non-dominated Sorting Genetic algorithm II (NSGA-II) model but is oriented toward the subgroup-discovery task using specific operators to promote the extraction of interpretable and high-quality subgroup-discovery rules. The proposal includes different mechanisms to improve diversity in the population and permits the use of different combinations of quality measures in the evolutionary process. An elaborate experimental study, which was reinforced by the use of nonparametric tests, was performed to verify the validity of the proposal, and the proposal was compared with other subgroup discovery methods. The results show that NMEEF-SD obtains the best results among several algorithms studied.

关键词： Descriptive rule induction fuzzy rules genetic fuzzy system multiobjective evolutionary algorithm subgroup discovery

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Improving the multiobjective evolutionary algorithm based on decomposition with new penalty schemes

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SOFT COMPUTING 2017年第16期21卷 4677-4691页

作者： Yang, Shengxiang Jiang, Shouyong Jiang, Yong Nanjing Univ Informat Sci & Technol Sch Math & Stat Nanjing 210044 Jiangsu Peoples R China De Montfort Univ Sch Comp Sci & Informat Ctr Computat Intelligence Leicester LE1 9BH Leics England

It has been increasingly reported that the multiobjective optimization evolutionary algorithm based on decomposition (MOEA/D) is promising for handling multiobjective optimization problems (MOPs). MOEA/D employs scalarizing functions to convert an MOP into a number of single-objective subproblems. Among them, penalty boundary intersection (PBI) is one of the most popular decomposition approaches and has been widely adopted for dealing with MOPs. However, the original PBI uses a constant penalty value for all subproblems and has difficulties in achieving a good distribution and coverage of the Pareto front for some problems. In this paper, we investigate the influence of the penalty factor on PBI, and suggest two new penalty schemes, i.e., adaptive penalty scheme and subproblem-based penalty scheme (SPS), to enhance the spread of Pareto-optimal solutions. The new penalty schemes are examined on several complex MOPs, showing that PBI with the use of them is able to provide a better approximation of the Pareto front than the original one. The SPS is further integrated into two recently developed MOEA/D variants to help balance the population diversity and convergence. Experimental results show that it can significantly enhance the algorithm's performance.

关键词： multiobjective evolutionary algorithm Decomposition Penalty boundary intersection Adaptive penalty scheme Subproblem-based penalty scheme

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Hybrid multiobjective evolutionary algorithm based on differential evolution for flow shop scheduling problems

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COMPUTERS & INDUSTRIAL ENGINEERING 2019年 130卷 661-670页

作者： Zhang, Wenqiang Wang, Yu Yang, Yang Gen, Mitsuo Henan Univ Technol Coll Informat Sci & Engn Zhengzhou Henan Peoples R China Shanghai Maritime Univ Sch Logist Engn Shanghai Peoples R China Tokyo Univ Sci Fuzzy Log Syst Inst Tokyo Japan

This paper proposes a hybrid multiobjective evolutionary algorithm based on differential evolution (HMOEA/DE) to solve the flow shop scheduling problems (FSPs) with the criteria of minimizing the makespan and tardiness simultaneously. Firstly, the hybrid multiobjective evolutionary algorithm (HMOEA) in HMOEA/DE is designed as the global search strategy, which rapidly improves the convergence and distribution performances towards the center and edge regions of Pareto frontier. Secondly, differential evolution (DE) strategy is combined with HMOEA as the local search mechanism to further enhance the convergence and distribution performances on the elite population obtained by HMOEA. Two DE mutation operators are designed for the individuals in the elite population: one is to further improve the performance of each individual and the other serves to much enhance the individual randomly. Numerical comparisons indicate that the efficacy of HMOEA/DE outperforms the traditional multiobjective evolutionary algorithm without DE in convergence and distribution performances on benchmark test problems and FSPs while verifying the advantages and disadvantages of different DE methods.

关键词： multiobjective evolutionary algorithm Differential evolution Flow shop scheduling problem

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A Dynamic multiobjective evolutionary algorithm Based on Decision Variable Classification

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第3期52卷 1602-1615页

作者： Liang, Zhengping Wu, Tiancheng Ma, Xiaoliang Zhu, Zexuan Yang, Shengxiang Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Shenzhen Pengcheng Lab Shenzhen 518055 Peoples R China Shenzhen Univ Shenzhen Inst Artificial Intelligence & Robot Soc SZU Branch Shenzhen 518060 Peoples R China De Montfort Univ Ctr Computat Intelligence Sch Comp Sci & Informat Leicester LE1 9BH Leics England

In recent years, dynamic multiobjective optimization problems (DMOPs) have drawn increasing interest. Many dynamic multiobjective evolutionary algorithms (DMOEAs) have been put forward to solve DMOPs mainly by incorporating diversity introduction or prediction approaches with conventional multiobjective evolutionary algorithms. Maintaining a good balance of population diversity and convergence is critical to the performance of DMOEAs. To address the above issue, a DMOEA based on decision variable classification (DMOEA-DVC) is proposed in this article. DMOEA-DVC divides the decision variables into two and three different groups in static optimization and changes response stages, respectively. In static optimization, two different crossover operators are used for the two decision variable groups to accelerate the convergence while maintaining good diversity. In change response, DMOEA-DVC reinitializes the three decision variable groups by maintenance, prediction, and diversity introduction strategies, respectively. DMOEA-DVC is compared with the other six state-of-the-art DMOEAs on 33 benchmark DMOPs. The experimental results demonstrate that the overall performance of the DMOEA-DVC is superior or comparable to that of the compared algorithms.

关键词： Sociology Statistics Heuristic algorithms Optimization Convergence evolutionary computation Benchmark testing Decision variable classification dynamic multiobjective evolutionary algorithm (DMOEA) dynamic multiobjective optimization problem (DMOP) multiobjective evolutionary algorithm multiobjective optimization problem (MOP)

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Optimal WCDMA network planning by multiobjective evolutionary algorithm with problem-specific genetic operation

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KNOWLEDGE AND INFORMATION SYSTEMS 2015年第3期45卷 679-703页

作者： Gu, Fangqing Liu, Hai-lin Cheung, Yiu-Ming Xie, Shengli Guangdong Univ Technol Guangzhou Guangdong Peoples R China Guangdong Univ Technol Sch Appl Math Guangzhou Guangdong Peoples R China Guangdong Univ Technol Inst Intelligent Informat Proc LI2P Guangzhou Guangdong Peoples R China Guangdong Univ Technol Guangdong key Lab Informat Technol Internet Thing Guangzhou Guangdong Peoples R China Guangdong Univ Technol Sch Automat Guangzhou Guangdong Peoples R China Hong Kong Baptist Univ Dept Comp Sci Hong Kong Hong Kong Peoples R China Beijing Normal Univ Hong Kong Baptist Univ United Int Coll Zhuhai Peoples R China

The wideband code division multiple access (WCDMA) network planning problem requires to determine the location and the configuration parameters of the base stations (BSs) so as to maximize the capacity and minimize the installation cost. This problem can be formulated as a complex set covering problem. Compared to the classical set covering problems, the coverage area of each BS is unknown in advance. This makes that the selection of each BS location and configuration parameters is determined by the location and configuration parameters of the neighbor BSs. Accordingly, we will conduct a competition and cooperation model based on the re-covered area of the BSs to measure the relationship of the BSs. Then, an efficient genetic operation based on this model is proposed to generate new-quality solutions. Further, four BS configuration parameters, i.e., the antenna height, antenna tilt, sector orientation and pilot signal power, are taken into account as well. Since there are too many combination levels of the configuration parameters, an encoding method based on orthogonal design is presented to reduce the search space. Subsequently, we merge the proposed encoding method and genetic operation into the multiobjective evolutionary algorithm-based decomposition (MOEA/D-M2M) to solve the WCDMA network planning problem. Simulation results show the efficacy of the proposed encoding and genetic operation in comparison with the existing counterpart.

关键词： Set covering problem Wireless network planning multiobjective evolutionary algorithm Orthogonal design

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A multiobjective evolutionary algorithm That Diversifies Population by Its Density

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2012年第2期16卷 149-172页

作者： Chow, Chi Kin Yuen, Shiu Yin City Univ Hong Kong Dept Elect Engn Kowloon Hong Kong Peoples R China

Most existing multiobjective evolutionary algorithms (MOEAs) assume the existence of Pareto-optimal solutions/Pareto-optimal objective vectors in a neighborhood of an obtained Pareto-optimal set (PS)/Pareto-optimal front (PF). Obviously, this assumption does not work well on the multiobjective problem (MOP) whose true PF and true PS are in the form of multiple segments-truly disconnected MOP (TYD-MOP). Moreover, these MOEAs commonly involve more than three control parameters;and some of them even involve nine control parameters. The stabilities of their performance against parameter settings are generally unknown. In this paper, we propose a MOEA, namely multiobjective density driven evolutionary algorithm (MODdEA), which can handle TYD-MOP. MODdEA stores all evaluated solutions by a binary space partitioning (BSP) tree. Benefiting from the BSP scheme, a fast solution density estimation by the archive is naturally obtained. MODdEA uses this estimated density together with the nondominated rank to probabilistically select mating individuals, which relaxes the neighborhood assumption on PF in a parameter-less manner. Moreover, two genetic operators, extended arithmetic crossover and diversified mutation, are proposed to enhance the explorative search ability of the algorithm. MODdEA is examined on two test problem sets. The first test set consists of six TYD-MOPs;the second test set consists of 17 benchmark MOPs which are commonly examined by the existing MOEAs. Comparing to 14 test MOEAs, MODdEA has superior performance on TYD-MOP and is competitive on MOP whose true PF and PS are one single connected segment.

关键词： Genetic operators for multiobjective problems multiobjective evolutionary algorithm parameter-less algorithm spatial density based population diversification truly disconnected Pareto set/front

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A decomposition-based multiobjective evolutionary algorithm with angle-based adaptive penalty

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APPLIED SOFT COMPUTING 2019年 74卷 190-205页

作者： Qiao, Junfei Zhou, Hongbiao Yang, Cuili Yang, Shengxiang Beijing Univ Technol Coll Automat Fac Informat Technol Beijing 100124 Peoples R China Beijing Key Lab Computat Intelligence & Intellige Beijing 100124 Peoples R China De Montfort Univ Sch Comp Sci & Informat Ctr Computat Intelligence Leicester LE1 9BH Leics England

A multiobjective evolutionary algorithm based on decomposition (MOEA/D) decomposes a multiobjective optimization problem (MOP) into a number of scalar optimization subproblems and optimizes them in a collaborative manner. In MOEA/D, decomposition mechanisms are used to push the population to approach the Pareto optimal front (POF), while a set of uniformly distributed weight vectors are applied to maintain the diversity of the population. Penalty-based boundary intersection (PBI) is one of the approaches used frequently in decomposition. In PBI, the penalty factor plays a crucial role in balancing convergence and diversity. However, the traditional PBI approach adopts a fixed penalty value, which will significantly degrade the performance of MOEA/D on some MOPs with complicated POFs. This paper proposes an angle-based adaptive penalty (AAP) scheme for MOEA/D, called MOEA/D-AAP, which can dynamically adjust the penalty value for each weight vector during the evolutionary process. Six newly designed benchmark MOPs and an MOP in the wastewater treatment process are used to test the effectiveness of the proposed MOEA/D-AAP. Comparison experiments demonstrate that the AAP scheme can significantly improve the performance of MOEA/D. (C) 2018 Elsevier B.V. All rights reserved.

关键词： multiobjective evolutionary algorithm Decomposition Penalty boundary intersection Angle-based adaptive penalty

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