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An Improved multi-objective optimization for Workflow Scheduling in Cloud Platform

网际网路技术学刊 2017年第3期18卷 589-599页

作者： Soma Prathibha B. Latha G. Sumathi

Cloud computing is a promising research domain used to enhance the performance of information systems and applications used at our daily life. Workflow scheduling in cloud platform is a complex optimization problem because of its heterogeneous nature, complex billing models and infinite resources requirements which has lead the users to look for an optimal or a suboptimal solution that satisfy multiple competing goals such as minimum makespan and energy efficient solutions. The focus of this paper is to schedule workflow applications, optimizing makespan and energy consumption. A high-performance method for scheduling workflow on cloud computing platform will be presented in this paper, called non-dominated sorting particle swarm optimization (NSPSO). The proposed method is evaluated on synthetic representation of real world scientific workflow applications. The simulation results shows that the proposed method can provide better workflow scheduling solution for cloud when compared with other state-of-the-art methods.

关键词： Cloud computing Workflow scheduling multi-objective optimization problem

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Computational Complexity Measures for Many-objective optimization problems

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Procedia Computer Science 2014年 36卷 185-191页

作者： David M. Curry Cihan H. Dagli Department of Engineering Management and Systems Engineering Missouri University of Science and Technology Rolla Missouri 65409

multi-objective optimization problems (MOPs) are commonly encountered in the study and design of complex systems. Pareto dominance is the most common relationship used to compare solutions in MOPs, however as the number of objectives grows beyond three, Pareto dominance alone is no longer satisfactory. These problems are termed “Many-objective optimization problems (MaOPs)”. While most MaOP algorithms are modifications of common MOP algorithms, determining the impact on their computational complexity is difficult. This paper defines computational complexity measures for these algorithms and applies these measures to a multi-objective Evolutionary Algorithm (MOEA) and its MaOP counterpart.

关键词： multi-objective optimization problem MOP many-objective optimization problem MaOP computational complexity multi- objective evolutionary algorithm MOEA

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Runtime analysis of immune-inspired hypermutation operators in evolutionary multi-objective optimization

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SWARM AND EVOLUTIONARY COMPUTATION 2021年 65卷 100934-100934页

作者： Huang, Zhengxin Zhou, Yuren Youjiang Med Univ Nationalities Lab Comp Sci & Math Baise 533000 Peoples R China Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Peoples R China

Immune systems inspired algorithms with hypermutation operators have achieved great success in solving real-world single-objective as well as multi-objective optimization problems. Compared to the application, however, the theoretical analysis, particularly on understanding immune-inspired hypermutation operators, is underdeveloped. The few existing theoretical studies mainly focused on single-objective optimization. In this paper, we present a theoretical study for the effectiveness of immune-inspired hypermutation operators in solving multi-objective optimization problems. We compare the expected runtime of a simple multi-objective evolutionary algorithm using four typical immune-inspired hypermutation operators and two classical mutation operators. The results on four bi-objective optimization problems widely used in theoretical analysis, namely LOTZ, COCZ, Plateau-MOP and PL, show that using immune-inspired hypermutation operators can always find the Pareto fronts in polynomial expected runtime, which is slower than the best known expected runtime of using classical mutation operators by at most a factor of n. Particularly, on Plateau-MOP and PL, using immune-inspired hypermutation operators can be exponentially faster. This runtime analysis can enhance the understanding of immune-inspired hypermutation operators on solving multi-objective optimization problems, and might be helpful for designing efficient multi-objective evolutionary algorithms in practice.

关键词： Artificial immune system multi-objective optimization problem Hypermutation operator Runtime analysis

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A novel clustering-based evolutionary algorithm with objective space decomposition for multi/many-objective optimization

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INFORMATION SCIENCES 2024年 677卷

作者： Zheng, Wei Tan, Yanyan Yan, Zeyuan Yang, Mingming Xian Univ Sci & Technol Coll Comp Sci & Technol 58 Yanta Rd Xian 710054 Shaanxi Peoples R China Shandong Normal Univ Sch Informat Sci & Engn Jinan 250358 Shandong Peoples R China Sun Yat Sen Univ Sch Environm Sci & Engn 135 Xingang Xi Rd Guangzhou 510275 Guangdong Peoples R China Chongqing Jiaotong Univ Sch Aeronaut 66 Xuefu Rd Chongqing 400074 Peoples R China

The framework of decomposing a multi -objective optimization problem (MOP) into some MOPs holds considerable promise. However, its advancement is constrained by numerous elements, including the incorrect segmentation of the subspaces and the challenges in balancing convergence and diversity. To address these issues, an objective space Decomposition and Clustering-based Evolutionary Algorithm (DCEA) is proposed in this paper. Specifically, DCEA employs K-means clustering to create an appropriate mating pool for each individual without the necessity to predetermine the number of clusters. Within each mating pool, the proposed adaptive evolutionary operator is applied to produce offspring for balancing the convergence and diversity. To enhance the accuracy of partitioning, a refined environmental selection approach utilizing supplementary weight vectors is developed. Additionally, by utilizing historical clustering data, a straightforward approach to periodically adjust reference vectors for the allocation of computational resources is proposed. In experiments, both MOPs and many-objective optimization problems (MaOPs) are used to test DCEA. A total of 27 MOPs and 30 MaOPs are involved and 16 state-of-the-art algorithms are employed to compare with DCEA. Comprehensive experiments show that DCEA is an effective algorithm for solving both MOPs and MaOPs.

关键词： Decomposition Convergence and diversity Clustering multi-objective optimization problem Many-objective optimization problem

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A Computational Intelligence Approach to System-of-Systems Architecting Incorporating multi-objective optimization

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Procedia Computer Science 2015年 44卷 86-94页

作者： David M. Curry Cihan H. Dagli Department of Engineering Management and Systems Engineering Missouri University of Science and Technology Rolla Missouri 65409

A computational intelligence approach to system-of-systems architecting is developed using multi-objective optimization. Such an approach yields a set of optimal solutions (the Pareto set) which has both advantages and disadvantages. The primary benefit is that a set of solutions provides a picture of the optimal solution space that a single solution cannot. The primary difficulty is making use of a potentially infinite set of solutions. Therefore, a significant part of this approach is the development of a method to model the solution set with a finite number of points allowing the architect to intelligently choose a subset of optimal solutions based on criteria outside of the given objectives. The approach developed incorporates a meta-architecture, multi-objective genetic algorithm, and a corner search to identify points useful for modeling the solution space. This approach is then applied to a network centric warfare problem seeking the optimum selection of twenty systems. Finally, using the same problem, it is compared to a hybrid approach using single-objective optimization with a fuzzy logic assessor to demonstrate the advantage of multi-objective optimization.

关键词： System-of-Systems SoS Architecting Genetic Algorithm GA multi-objective optimization problem MOP multi-objective Evolutionary Algorithm MOEA Meta-Architecture

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Design of Pareto-Optimal Radar Receive Filters

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INTERNATIONAL JOURNAL OF ELECTRONICS AND TELECOMMUNICATIONS 2011年第4期57卷 477-481页

作者： De Maio, Antonio Piezzo, Marco Iommelli, Salvatore Farina, Alfonso Univ Napoli Federico II Via Claudio 21 I-80125 Naples Italy SELEX Sistemi Integrati I-00131 Rome Italy

This paper deals with the design of radar receive filters jointly optimized with respect to sidelobe energy and sidelobe peaks via Pareto-optimal theory. We prove that this criterion is tantamount to jointly minimizing two quadratic forms, so that the design can be analytically formulated in terms of a multi-objective optimization problem. In order to solve it, we resort to the scalarization technique, which reduces the vectorial problem into a scalar one using a Pareto weight defining the relative importance of the two objective functions. At the analysis stage, we assess the performance of the receive filters in correspondence of different values of the Pareto weight highlighting the performance compromises between the Integrated Sidelobe Level (ISL) and the Peak Sidelobe Level (PSL).

关键词： Radar receive filter design mismatched filter design multi-objective optimization problem Pareto-optimal points

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ECONOMIC-STATISTICAL DESIGN APPROACH FOR A VSSI X-BAR CHART CONSIDERING TAGUCHI LOSS FUNCTION AND RANDOM PROCESS SHIFTS

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INTERNATIONAL JOURNAL OF RELIABILITY QUALITY AND SAFETY ENGINEERING 2014年第2期21卷 1450006.1-1450006.23页

作者： Lupo, Toni Univ Palermo Dept Chem Management Informat & Mech Engn I-90128 Palermo Italy

Economic design approaches of control charts are commonly based on the assumption that various cost parameters values and the occurrence risk of assignable causes have to be a priori known with precision. However, in real operative contexts, such parameters can be really difficult to accurately estimate, especially considering costs arising from out-of-control conditions of the process. As consequence, pure economic design approaches can involve chart schemes with low statistical performance. To overcome such limitation, it is herein proposed a multi-objective economic-statistical design approach for an adaptive X-bar chart. In particular, such approach aims at the minimization of both the total quality related costs and the out-of-control average run length, in such a way assuring an optimal trade-off between economic and statistical performance of the related control procedure. Moreover, for a robust design approach, the mean shift is considered as a random variable. A mixed integer nonlinear constrained mathematical model is formulated to solve the treated problem, whereas the Pareto optimal frontier is described by the e-constraint method. In order to show the employment of the proposed approach, an illustrative example is developed and the related considerations are given. Finally, some sensitivity analysis is also performed to investigate the effects of operative and costs parameters on chart parameters.

关键词： Statistical process control adaptive X-bar control chart multi-objective optimization problem e-constraint method

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Comparative Study of Ant Colony Algorithms for multi-objective optimization

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INFORMATION 2019年第1期10卷 11页

作者： Ning, Jiaxu Zhang, Changsheng Sun, Peng Feng, Yunfei Shenyang Ligong Univ Sch Informat Sci & Engn Shenyang 110159 Liaoning Peoples R China Northeastern Univ Sch Comp Sci & Engn Shenyang 110819 Liaoning Peoples R China Iowa State Univ Dept Comp Sci Ames IA 50010 USA Sams Club Technol Wal Mart Inc Bentonville AR 72712 USA

In recent years, when solving MOPs, especially discrete path optimization problems, MOACOs concerning other meta-heuristic algorithms have been used and improved often, and they have become a hot research topic. This article will start from the basic process of ant colony algorithms for solving MOPs to illustrate the differences between each step. Secondly, we provide a relatively complete classification of algorithms from different aspects, in order to more clearly reflect the characteristics of different algorithms. After that, considering the classification result, we have carried out a comparison of some typical algorithms which are from different categories on different sizes TSP (traveling salesman problem) instances and analyzed the results from the perspective of solution quality and convergence rate. Finally, we give some guidance about the selection of these MOACOs to solve problem and some research works for the future.

关键词： multi-objective optimization problem multi-objective optimization algorithm meta-heuristic algorithm multi-objective ant colony optimization

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A Novel Quantum-inspired multi-objective Evolutionary Algorithm Based on Cloud Theory

A Novel Quantum-inspired Multi-Objective Evolutionary Algori...

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第五届ABB杯全国自动化系统工程师论文大赛

作者： Bo Xu Wang Cheng Jian-Ping Yu Yong Wang Department of Computer Science and Technology Guangdong University of Petrochemical Technology

In the previous papers,Quantum-inspired multi-objective evolutionary algorithm(QMEA)was proved to be better than conventional genetic algorithms for multi-objective optimization *** improve the quality of the nondominated set as well as the diversity of population in multi-objective problems,in this paper,a Novel Cloud-based quantum-inspired multi-objective evolutionary Algorithm(CQMEA)is *** is proposed by employing the concept and principles of Cloud *** algorithm utilizes the random orientation and stability of the cloud model,uses a self-adaptive mechanism with cloud model of Quantum gates updating strategy to implement global search efficient By using the self-adaptive mechanism and the better solution which is determined by the membership function uncertainly,Compared with several wellknown algorithms such as NSGA-Ⅱ,*** results show that(CQMEA)is more effective than QMEA and NSGA-Ⅱ.

关键词： multi-objective optimization problem Quantum-Inspired multi-objective Evolutionary Algorithm Cloud Model Evolutionary Algorithm

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A Surrogate-assisted Memetic Algorithm for Interval multi-objective optimization

A Surrogate-assisted Memetic Algorithm for Interval Multi-ob...

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IEEE Symposium Series on Computational Intelligence

作者： Jing Sun Zhuang Miao Dunwei Gong School of Sciences Huaihai Institute of Technology Lianyungang China School of Information and Control Engineering China University of Mining and Technology Xuzhou China China School of Information Science and Technology Qingdao University of Science and Technology

Interval multi-objective optimization problems (IMOPs) are ubiquitous and challenging. There are many optimizers for solving them;however, their drawbacks, such as the high computational cost and big uncertainty of the final front, hinder their applications in real-world situation. This paper proposes a surrogate-assisted interval multi-objective memetic algorithm (SA-IMOMA) that incorporates a surrogate model into the local search. In the framework of interval multi-objective memetic algorithms (IMOMAs), the fitness function of a local search is first defined by both the contribution of an individual to hyper-volume and the imprecision of the individual, and then a support vector machine (SVM) is trained and employed to evaluate local individuals so as to cut down the high computational cost of IMOMAs and further reduce the imprecision of the final front. The proposed algorithm was tested on 10 benchmark IMOPs and an IMOP in solar desalination. The empirical results indicate that SA-IMOMA is more economical than non-surrogate IMOMAs and superior to non-local-search IP-MOEA.

关键词： multi-objective optimization problem Interval Memetic algorithm Surrogate model

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