An Improved Multi-Objective Workflow Scheduling Using NSPSO With Fuzzy Rules

Abstract Lot of scientific problems in various domains from modelling sky as mosaics to understand Genome sequencing in biological applications are modelled as workflows with large number of interconnected tasks. Particle Swarm Optimization (PSO) based metaheuristics are currently used to address many optimization problems as they are simple to implement and able to produce quickly optimal or sub-optimal solutions based on learning capabilities. Even though many works are cited in the literature on workflow scheduling, most of the existing works are focused on reducing the makespan alone. Moreover, energy efficiency is considered only in few works included in the literature. Constraints about the dynamic workload allocation are not introduced in the existing systems. Moreover, the optimization techniques used in the existing systems have improved the QoS with little scalability in the cloud environment since they consider only the infrastructure as the service model.In this work a new algorithm has been proposed based on the proposal of a new Multi-Objective Optimization model called F-NSPSO using NSPSO Meta-Heuristic s. This method allows the user to choose a suitable configuration dynamically. An average of above 15% in the energy reduction for the proposed system over simple DVFS was achieved for all types of workflow applications with different dimensions. Similarly when compared to NSPSO an energy reduction of at least 10% has been observed for F-NSPSO for all three types of workflow applications. Compared to NSPSO algorithm F-NSPSO algorithm shows at least 13%, 12% and 21% improvement in average makespan for Montage, Cybershake and Epigenomics workflow applications respectively.

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Efficient Computation of Probabilistic Dominance in Multi-objective Optimization

ACM Transactions on Evolutionary Learning and Optimization ◽

10.1145/3469801 ◽

2021 ◽

Vol 1 (4) ◽

pp. 1-26

Author(s):

Faramarz Khosravi ◽

Alexander Rass ◽

Jürgen Teich

Keyword(s):

Optimization Problems ◽

Statistical Tests ◽

Optimization Techniques ◽

Simultaneous Optimization ◽

Sampled Data ◽

Efficient Computation ◽

Multi Objective Optimization ◽

Multi Objective ◽

Conflicting Objectives ◽

Comparison Operator

Real-world problems typically require the simultaneous optimization of multiple, often conflicting objectives. Many of these multi-objective optimization problems are characterized by wide ranges of uncertainties in their decision variables or objective functions. To cope with such uncertainties, stochastic and robust optimization techniques are widely studied aiming to distinguish candidate solutions with uncertain objectives specified by confidence intervals, probability distributions, sampled data, or uncertainty sets. In this scope, this article first introduces a novel empirical approach for the comparison of candidate solutions with uncertain objectives that can follow arbitrary distributions. The comparison is performed through accurate and efficient calculations of the probability that one solution dominates the other in terms of each uncertain objective. Second, such an operator can be flexibly used and combined with many existing multi-objective optimization frameworks and techniques by just substituting their standard comparison operator, thus easily enabling the Pareto front optimization of problems with multiple uncertain objectives. Third, a new benchmark for evaluating uncertainty-aware optimization techniques is introduced by incorporating different types of uncertainties into a well-known benchmark for multi-objective optimization problems. Fourth, the new comparison operator and benchmark suite are integrated into an existing multi-objective optimization framework that features a selection of multi-objective optimization problems and algorithms. Fifth, the efficiency in terms of performance and execution time of the proposed comparison operator is evaluated on the introduced uncertainty benchmark. Finally, statistical tests are applied giving evidence of the superiority of the new comparison operator in terms of \epsilon -dominance and attainment surfaces in comparison to previously proposed approaches.

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An Overview of Few Nature Inspired Optimization Techniques and Its Reliability Applications

International Journal of Mathematical Engineering and Management Sciences ◽

10.33889/ijmems.2020.5.4.058 ◽

2020 ◽

Vol 5 (4) ◽

pp. 732-743

Author(s):

Nitin Uniyal ◽

Sangeeta Pant ◽

Anuj Kumar

Keyword(s):

Optimization Problems ◽

Optimization Techniques ◽

Multi Objective Optimization ◽

Multi Objective ◽

Research Fields ◽

Research Problems ◽

Nature Inspired Algorithms ◽

New Algorithms ◽

Single Objective

Optimization has been a hot topic due to its inevitably in the development of new algorithms in almost every applied branch of Mathematics. Despite the broadness of optimization techniques in research fields, there is always an open scope of further refinement. We present here an overview of nature-inspired optimization with a subtle background of fundamentals and classification and their reliability applications. An attempt has been made to exhibit the contrast nature of multi objective optimization as compared to single objective optimization. Though there are various techniques to achieve the optimality in optimization problems but nature inspired algorithms have proved to be very efficient and gained special attention in modern research problems. The purpose of this article is to furnish the foundation of few nature inspired optimization techniques and their reliability applications to an interested researcher.

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Mixture Surrogate Models for Multi- Objective Optimization

International Journal of Engineering Sciences ◽

10.36224/ijes.130104 ◽

2020 ◽

Vol 13 (1) ◽

Author(s):

Shailesh S. Kadre ◽

Vipin K. Tripathi

Keyword(s):

Finite Element ◽

Optimization Problems ◽

Surrogate Models ◽

Optimization Techniques ◽

Optimization Process ◽

Multiple Model ◽

Multi Objective Optimization ◽

Multi Objective ◽

Costly Function ◽

Selection Of

Multi-objective optimization problems (MOOP) involve minimization of more than one objective functions and all of them are to be simultaneously minimized. The solution of these problems involves a large number of iterations. The multi- objective optimization problems related structural optimization of complex engineering structures is usually solved with finite element analysis (FEA). The solution time required to solve these FEA based solutions are very high. So surrogate models or meta- models are used to approximate the finite element solution during the optimization process. These surrogate assisted multi- objective optimization techniques are very commonly used in the current literature. These optimization techniques use evolutionary algorithm and it is very difficult to guarantee the convergence of the final solution, especially in the cases where the budget of costly function evaluations is low. In such cases, it is required to increase the efficiency of surrogate models in terms of accuracy and total efforts required to find the final solutions.In this paper, an advanced surrogate assisted multi- objective optimization algorithm (ASMO) is developed. This algorithm can handle linear, equality and non- linear constraints and can be applied to both benchmark and engineering application problems. This algorithm does not require any prior knowledge for the selection of surrogate models. During the optimization process, best single and mixture surrogate models are automatically selected. The advanced surrogate models are created by MATSuMoTo, the MATLAB based tool box. These mixture models are built by Dempster- Shafer theory (DST). This theory has a capacity to handle multiple model characteristics for the selection of best models. By adopting this strategy, it is ensured that most accurate surrogate models are selected. There can be different kind of surrogate models for objective and constraint functions. Multi-objective optimization of machine tool spindle is studied as the test problem for this algorithm and it is observed that the proposed strategy is able to find the non- dominated solutions with minimum number of costly function evaluations. The developed method can be applied to other benchmark and engineering applications.

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A Manufacturing Oriented Single Point Search Hyper-Heuristic Scheme for Multi-Objective Optimization

Volume 2B: 43rd Design Automation Conference ◽

10.1115/detc2017-68265 ◽

2017 ◽

Cited By ~ 3

Author(s):

Pei Cao ◽

Zhaoyan Fan ◽

Robert Gao ◽

Jiong Tang

Keyword(s):

Simulated Annealing Algorithm ◽

Optimization Problems ◽

Search Algorithm ◽

Single Point ◽

Optimization Techniques ◽

Multi Objective Optimization ◽

Nsga Ii ◽

Practical Applications ◽

Multi Objective ◽

Point Search

Multi-objective optimization problems are frequently encountered in engineering analyses. Optimization techniques in practical applications are devised and evaluated mostly for specific problems, and thus may not be generally applicable when applications vary. In this study we formulate a probability matching based hyper-heuristic scheme, then propose four low-level heuristics which can work coherently with the single point search algorithm MOSA/R (Multi-Objective Simulated Annealing Algorithm based on Re-pick) towards multi-objective optimization problems of various properties, namely DTLZ and UF test instances. Making use of the domination amount, crowding distance and hypervolume calculations, the hyper-heuristic scheme could meet different optimization requirements. The approach developed (MOSA/R-HH) exhibits better and more robust performance compared to AMOSA, NSGA-II and MOEA/D as illustrated in the numerical tests. The outcome of this research may potentially benefit various design and manufacturing practices.

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Collaborative Optimization of Excavator Boom Mechanism Parameters under Multiple Loading Conditions

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.655-657.390 ◽

2013 ◽

Vol 655-657 ◽

pp. 390-395

Author(s):

Guang Qiu Yin ◽

Shu He Zheng ◽

Shu Wen Lin

Keyword(s):

Optimization Problems ◽

Optimization Technique ◽

Systems Design ◽

Optimization Techniques ◽

Collaborative Optimization ◽

Multi Objective Optimization ◽

Design And Optimization ◽

Multi Objective ◽

Coupling System ◽

Multiple Loading

On the base of explaining Multi-Objective Optimization Problems (MOOP) and Collaborative Optimization Techniques (COT), this paper puts forward an original and efficient approach—Collaborative Optimization Technique for Multi-Objective Optimization Problems (COTMOOP)—which combines Collaborative Optimization (CO) techniques and Evolutionary Algorithms (EAs). It has been developed so as to facilitate the organizations and increase the performances of complex coupling systems design and optimization. The newly method has been succeeded in applying to the design and optimization of excavator boom mechanism that is a complex coupling system. The optimal solutions can prove that the approach is efficient and practical.

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