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A pattern-driven solution for designing multi-objective evolutionary algorithms

NATURAL COMPUTING 2020年第3期19卷 481-494页

作者： Guizzo, Giovani Vergilio, Silvia R. DInf Fed Univ Parana CP 19081 BR-19031970 Curitiba PR Brazil

multi-objective evolutionary algorithms (MOEAs) have been widely studied in the literature, which led to the development of several frameworks and techniques to implement them. Consequently, the reusability, scalability and maintainability became fundamental concerns in the development of such algorithms. To this end, the use of design patterns (DPs) can benefit, ease and improve the design of MOEAs. DPs are reusable solutions for common design problems, which can be applied to almost any context. Despite their advantages to decrease coupling, increase flexibility, and allow an easier design extension, DPs have been underexplored for MOEA design. In order to contribute to this research topic, we propose a pattern-driven solution for the design of MOEAs. The MOEA designed with our solution is compared to another MOEA designed without it. The comparison considered: the Integration and Test Order (ITO) problem and the Traveling Salesman problem (TSP). Obtained results show that the use of this DP-driven solution allows the reuse of MOEA components, without decreasing the quality, in terms of hypervolume. This means that the developer can extend the algorithms to include other components using only object-oriented mechanisms in an easier way, while maintaining the expected results.

关键词： Meta-heuristic design pattern multi-objective evolutionary algorithm Software testing Hyper-heuristic

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A fuzzy based approach for fitness approximation in multi-objective evolutionary algorithms

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2015年第5期29卷 2111-2131页

作者： Pourbahman, Zahra Hamzeh, Ali Shiraz Univ Dept Elect & Comp Engn Shiraz Iran

evolutionary algorithm provides a framework that is largely applicable to particular problems including multiobjective optimization problems, basically for the ease of their implementation and their capability to perform efficient parallel search. Indeed, in some cases, expensive multiobjective optimization evaluations might be a challenge to restrict the number of explicit fitness evaluations in multiobjective evolutionary algorithms. Accordingly, this article presents a novel approach that tackles this problem so as to not only decrease the number of fitness evaluations but also to improve the performance. During evolution, our proposed approach selects fit individuals based on the knowledge acquired throughout the search, and performs explicit fitness evaluations on these individuals. A comprehensive comparative analysis of a wide range of well-established test problems, selected from both traditional and state-of-the-art benchmarks, has been presented. Afterward, the effectiveness of the obtained results is compared with some of the state-of-the-art methods using two well-known metrics-i.e. Hypervolume and Inverted Generational Distance (IGD). The experiments of our implemented approach is performed to illustrate that our proposal seems to be promising and would prove more efficient than other approaches in terms of both the performance and the computational cost.

关键词： Fitness approximation multi-objective evolutionary algorithm fuzzy inference system hypervolume approximation hypervolume contribution

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Data Structures in multi-objective evolutionary algorithms

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Journal of Computer Science & Technology 2012年第6期27卷 1197-1210页

作者： Najwa Altwaijry Mohamed El Bachir Menai Department of Computer Science College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178 Riyadh 11453Saudi Arabia

Data structures used for an algorithm can have a great impact on its performance, particularly for the solution of large and complex problems, such as multi-objective optimization problems （MOPs）. multi-objective evolutionary algorithms （MOEAs） are considered an attractive approach for solving MOPs~ since they are able to explore several parts of the Pareto front simultaneously. The data structures for storing and updating populations and non-dominated solutions （archives） may affect the efficiency of the search process. This article describes data structures used in MOEAs for realizing populations and archives in a comparative way, emphasizing their computational requirements and general applicability reported in the original work.

关键词： multi-objective evolutionary algorithm data structure Pareto front archive population

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A new stopping criterion for multi-objective evolutionary algorithms: application in the calibration of a hydrologic model

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COMPUTATIONAL GEOSCIENCES 2019年第6期23卷 1219-1235页

作者： Ticona Gutierrez, Juan Carlos Adamatti, Daniela Santini Bravo, Juan Martin Univ Fed Rio Grande do Sul Inst Pesquisas Hidraul Porto Alegre RS Brazil

multi-objective genetic algorithms have been successfully applied in a wide variety of problems. Although widely used, there are few theoretical guidelines for determining when to stop the search. Many users commonly use rules like stopping when there is no significant improvement during the last generations or when a certain number of generations are reached. In this paper, we propose a new stopping criterion approach and evaluate its performance with three widely used evolutionary algorithms in the calibration of a hydrologic model. The stopping criterion is based on the minimum number of generations required to achieve a determined number of non-dominated solutions in Pareto Front. The new stopping criterion was tested in the lumped hydrologic model IPH-II calibration, using the genetic algorithms NSGA-II, NSGA-III, and SPEA-II and two objective functions. The generational distance, spacing, and maximum spread metrics were used to assess the performance of the proposed stopping criterion in comparison to the standard criterion. Results show no significant loss in goodness of fit associated with the proposed stopping criterion, both in calibration and validation periods. Performance metrics have shown similar values when the standard and the proposed stopping criteria were compared. However, the average computational time to complete the optimization process was reduced up to 38.2% when the proposed stopping criterion was used. Thus, it can be concluded that the new stopping criterion reduces the iteration workload without compromising the accuracy of solution sets.

关键词： multi-objective evolutionary algorithm Lumped hydrologic model Stopping criterion NSGA-II NSGA-III SPEA-II

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A co-evolutionary algorithm based on sparsity clustering for sparse large-scale multi-objective optimization

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年第PartB期133卷

作者： Zhang, Yajie Wu, Chengming Tian, Ye Zhang, Xingyi Anhui Univ Sch Comp Sci & Technol Hefei Peoples R China Anhui Univ Sch Artificial Intelligence Hefei Peoples R China Anhui Univ Inst Phys Sci & Informat Technol Hefei Peoples R China Anhui Univ Informat Mat & Intelligent Sensing Lab Anhui Prov Hefei Peoples R China

Sparse large-scale multi -objective optimization problems (LSMOPs), which are characterized by high dimensional search space and sparse Pareto optimal solutions, have a widespread existence in academic research and practical applications. While the high dimensional decision space poses challenges to multi -objective evolutionary algorithms (MOEAs), the difficulty of solving sparse LSMOPs can be alleviated by utilizing the prior knowledge that the optimal solutions are sparse. In this paper, a co -evolutionary algorithm based on sparsity clustering, namely SCEA, is proposed, where the prior knowledge of sparse optimal solutions is utilized explicitly. At each generation, SCEA first calculates the current optimal sparsity by sparsity clustering. Then, SCEA divides the population into a winner subpopulation and two loser subpopulations. While the winner subpopulation reproduces offspring solutions by conventional genetic operators, the loser subpopulations generate offspring solutions along two competitive directions under the guidance of current optimal sparsity and variable importance. In the experiments, four state-of-the-art MOEAs are selected as the comparative algorithms. Experimental results show that the proposed algorithm is superior to the four competitors on both benchmark problems and practical applications, which include the sparse signal reconstruction problem, the community detection problem, and the instance selection problem.

关键词： multi-objective evolutionary algorithm Large-scale multi-objective optimization Signal reconstruction Community detection Instance selection

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Do Search and Selection Operators Play Important Roles in multi-objective evolutionary algorithms:A Case Study

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Wuhan University Journal of Natural Sciences 2003年第S1期8卷 195-201页

作者： Yan Zhen-yu, Kang Li-shan, Lin Guang-ming ,He MeiState Key Laboratory of Software Engineering, Wuhan University, Wuhan 430072, Hubei, ChinaSchool of Computer Science, UC, UNSW Australian Defence Force Academy, Northcott Drive, Canberra, ACT 2600 AustraliaCapital Bridge Securities Co. ,Ltd, Floor 42, Jinmao Tower, Shanghai 200030, China Wuhan University State Key Laboratory of Software Engineering Wuhan Hubei China (GRID:grid.49470.3e) (ISNI:***) UC UNSW Australian Defence Force Academy School of Computer Science Canberra Australia (GRID:grid.97008.36) (ISNI:***) Capital Bridge Securities Co. Ltd Shanghai China (GRID:grid.97008.36)

multi-objective evolutionary algorithm (MOEA) is becoming a hot research area and quite a few aspects of MOEAs have been studied and discussed. However there are still few literatures discussing the roles of search and selection operators in MOEAs. This paper studied their roles by solving a case of discrete multi-objective Optimization Problem (MOP): multi-objective TSP with a new MOEA. In the new MOEA, We adopt an efficient search operator, which has the properties of both crossover and mutation, to generate the new individuals and chose two selection operators: Family Competition and Population Competition with probabilities to realize selection. The simulation experiments showed that this new MOEA could get good uniform solutions representing the Pareto Front and outperformed SPEA in almost every simulation run on this problem. Furthermore, we analyzed its convergence property using finite Markov chain and proved that it could converge to Pareto Front with probability 1. We also find that the convergence property of MOEAs has much relationship with search and selection operators.

关键词： multi-objective evolutionary algorithm convergence property analysis search operator selection operator Markov chain

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A computation offloading algorithm based on multi-objective evolutionary optimization in mobile edge computing

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第1期121卷

作者： Chai, Zheng-Yi Liu, Xu Li, Ya-Lun Tiangong Univ Sch Comp Sci & Technol Tianjin 300387 Peoples R China Tiangong Univ Sch Software Tianjin 300387 Peoples R China Tiangong Univ Tianjin Key Lab Autonomous Intelligence Technol & Tianjin 300387 Peoples R China Tiangong Univ Sch Elect & Informat Engn Tianjin 300387 Peoples R China

For computation offloading problem (COP) in mobile edge computing (MEC), the energy consumption of terminal equipments(TEs) and the delay of mobile equipment applications are two optimization goals. In real life, terminal equipment is dynamic, and their number, mobility, and continuous changes in wireless channels will affect the balance between the mentioned energy consumption and delay. Different from available works, we model the COP in MEC as a dynamic multi-objective problem (DMOP) in this paper, and propose an improved dynamic multi-objective evolutionary optimization based on decomposition (DMOEA/D) to solve it, namely DMOEA/D-COPMEC. In the proposed algorithm, the environmental change is detected by a fixed detector, and whether the current change is similar to the historical change is determined. If so, the difference prediction is used to re-locate the population individual in the new MEC environment, otherwise, the memory -based strategy is used to response environmental change. In MOEA/D, an adaptive weight adjustment strategy based on chain segmentation (CS) is adopted to generate a set of uniformly distributed weight vectors. The simulation results show that the proposed algorithm can better balance the application delay and the terminal energy consumption if there is environment change. The solution set is closer to reality and better than the related algorithms.

关键词： Computation offloading Dynamic multi-objective optimization Mobile edge computing multi-objective evolutionary algorithm (MOEA)

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Optimization of a Demand Responsive Transport Service Using multi-objective evolutionary algorithms 19

Optimization of a Demand Responsive Transport Service Using ...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Viana, Renan J. S. Santos, Andre G. Martins, Flavio V. C. Wanner, Elizabeth F. CEFET MG Programa Posgrad Modelagem Matemat & Computac Belo Horizonte MG Brazil Univ Fed Vicosa Vicosa MG Brazil Ctr Fed Educ Tecnol Minas Gerais Belo Horizonte MG Brazil Aston Univ Birmingham W Midlands England

ISBN: (纸本)9781450367486

This paper addresses the problem of optimizing a Demand Responsive Transport (DRT) service. A DRT is a flexible transportation service that provides on-demand transport for users who formulate requests specifying desired locations and times of pick-up and delivery. The vehicle routing and scheduling procedures are performed based on a set of requests. This problem is modeled as a multi-objective Dial-a-Ride problem (DARP), in which a set of objectives related to costs and user inconvenience is optimized while respecting a set of constraints imposed by the passengers and vehicles, as time windows and capacity. The resulting model is solved by means of three multi-objective evolutionary algorithms (MOEA) associated with feasibility-preserving operators. Computational experiments were performed on benchmark instances and the results were analyzed by means of performance quality indicators widely used for multi-objective algorithms comparison. The proposed approaches demonstrate efficient and higher performance when optimizing this DRT service compared to another algorithm from the literature.

关键词： Demand Responsive Transport Dial-a-Ride Problem multi-objective evolutionary algorithm

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BICLUSTERING ANALYSIS OF GENE EXPRESSION DATA USING multi-objective evolutionary algorithmS 14

BICLUSTERING ANALYSIS OF GENE EXPRESSION DATA USING MULTI-OB...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Golchin, Maryam Davarpanah, Seyed Hashem Liew, Alan Wee-Chung Griffith Univ Sch Informat & Commun Technol Nathan Qld 4111 Australia

ISBN: (纸本)9781467372206

Clustering is an unsupervised learning technique that groups data into clusters using the entire conditions. However, sometimes, data is similar only under a subsetof conditions. Biclustering allows clustering of rows and columns of a dataset simultaneously. It extracts more accurate information from sparse datasets. In recent years, biclustering has found many useful applications in different fields and many biclustering algorithms have been proposed. Using both row and column information of data, biclustering requires the optimization of two conflicting objectives. In this study, a new multi-objective evolutionary biclustering framework using SPEA2 is proposed. A heuristic local search based on the gene and condition deletion and addition is added into SPEA2 and the best bicluster is selected usinga new quantitative measure that considers both its coherence and size. The performance of our algorithm is evaluatedusing simulated and gene expression data and compared with several well-known biclustering methods. The experimental results demonstrate better performance with respect to the size and MSR of detected biclusters and significant enrichment of detected genes.

关键词： Biclustering Gene expression data multi-objective evolutionary algorithm SPEA2

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What Performance Indicators to Use for Self-Adaptation in multi-objective evolutionary algorithms

What Performance Indicators to Use for Self-Adaptation in Mu...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Ye, Furong Neumann, Frank de Nobel, Jacob Neumann, Aneta Back, Thomas Chinese Acad Sci ISCAS Beijing Peoples R China Univ Adelaide Adelaide SA Australia Leiden Univ LIACS Leiden Netherlands

ISBN: (纸本)9798400704949

Parameter control has succeeded in accelerating the convergence process of evolutionary algorithms. While empirical and theoretical studies have shed light on the behavior of algorithms for single-objective optimization, little is known about how self-adaptation influences multi-objective evolutionary algorithms. In this work, we contribute (1) extensive experimental analysis of the Global Simple evolutionary multi-objective algorithm (GSEMO) variants on classic problems, such as OneMinMax, LOTZ, COCZ, and (2) a novel version of GSEMO with self-adjusting mutation rates. To enable self-adaptation in GSEMO, we explore three techniques from single-objective optimization for self-adjusting mutation rates and use various performance metrics, such as hypervolume and inverted generational distance, to guide the adaptation. Our experiments show that adapting the mutation rate based on single-objective optimization and hypervolume can speed up the convergence of GSEMO. Moreover, we demonstrate that a GSEMO with self-adjusting mutation rates, which focuses on optimizing one of the objectives alternatively and adjusts the mutation rate for each solution individually, can outperform the GSEMO with static mutation rates across the tested problem. This work provides a comprehensive benchmarking study for MOEAs and complements existing theoretical runtime analysis. Our proposed algorithm addresses interesting issues for designing MOEAs for future practical applications.

关键词： multi-objective evolutionary algorithm self-adjusting mutation benchmarking

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