Reference Point Based Multi-Objective Optimization to Workflow Grid Scheduling

2012 ◽  
Vol 3 (1) ◽  
pp. 80-99 ◽  
Author(s):  
Ritu Garg ◽  
Awadhesh Kumar Singh
Keyword(s):  
Reference Point ◽  
Simulation Analysis ◽  
Computation Time ◽  
Grid Scheduling ◽  
Nsga Ii ◽  
Multi Objective ◽  
Global Computing ◽  
Conflicting Objectives ◽  
Multiple Regions ◽  
Np Hard Problem

Grid provides global computing infrastructure for users to avail the services supported by the network. The task scheduling decision is a major concern in heterogeneous grid computing environment. The scheduling being an NP-hard problem, meta-heuristic approaches are preferred option. In order to optimize the performance of workflow execution two conflicting objectives, namely makespan (execution time) and total cost, have been considered here. In this paper, reference point based multi-objective evolutionary algorithms, R-NSGA-II and R-e-MOEA, are used to solve the workflow grid scheduling problem. The algorithms provide the preferred set of solutions simultaneously, near the multiple regions of interest that are specified by the user. To improve the diversity of solutions we used the modified form of R-NSGA-II (represented as M-R-NSGA-II). From the simulation analysis it is observed that, compared to other algorithms, R-e-MOEA delivers better convergence, uniform spacing among solutions keeping the computation time limited.

scholarly journals Exploiting Trade-Off Criteria to Improve the Efficiency of Genetic Multi-Objective Optimisation Algorithms

2021 ◽  
Vol 3 ◽  
Author(s):  
Viviane De Buck ◽  
Philippe Nimmegeers ◽  
Ihab Hashem ◽  
Carlos André Muñoz López ◽  
Jan Van Impe
Keyword(s):  
Pareto Front ◽  
Batch Reactor ◽  
Computation Time ◽  
Scalar Case ◽  
Stopping Criterion ◽  
Nsga Ii ◽  
Trade Off ◽  
Multi Objective ◽  
Conflicting Objectives ◽  
Novel Strategy

The highly competitive nature of the chemical industry requires the optimisation of the design and exploitation of (bio-)chemical processes with respect to multiple, often conflicting objectives. Genetic algorithms are widely used in the context of multi-objective optimisation due to their overall straightforward implementation and numerous other advantages. NSGA-II, one of the current state-of-the-art algorithms in genetic multi-objective optimisation has, however, two major shortcomings, inherent to evolutionary algorithms: 1) the inability to distinguish between solutions based on their mutual trade-off and distribution; 2) a problem-irrelevant stopping criterion based on a maximum number of iterations. The former results in a Pareto front that contains redundant solutions. The latter results in an unnecessary high computation time. In this manuscript, a novel strategy is presented to overcome these shortcomings: t-domination. t-domination uses the concept of regions of practically insignificant trade-off (PIT-regions) to distinguish between solutions based on their trade-off. Two solutions that are located in each other’s PIT-regions are deemed insignificantly different and therefore one can be discarded. Additionally, extrapolating the concept of t-domination to two subsequent solution populations results in a problem-relevant stopping criterion. The novel algorithm is capable of generating a Pareto front with a trade-off-based solution resolution and displays a significant reduction in computation time in comparison to the original NSGA-II algorithm. The algorithm is illustrated on benchmark scalar case studies and a fed-batch reactor case study.

Multi Objective Design Optimization of Two Bar Truss Using NSGA II and TOPSIS

2014 ◽  
Vol 984-985 ◽  
pp. 419-424
Author(s):  
P. Sabarinath ◽  
M.R. Thansekhar ◽  
R. Saravanan
Keyword(s):  
Design Optimization ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Optimal Solutions ◽  
Objective Functions ◽  
Nsga Ii ◽  
Multi Objective ◽  
Total Displacement ◽  
Design Variables ◽  
Conflicting Objectives

Arriving optimal solutions is one of the important tasks in engineering design. Many real-world design optimization problems involve multiple conflicting objectives. The design variables are of continuous or discrete in nature. In general, for solving Multi Objective Optimization methods weight method is preferred. In this method, all the objective functions are converted into a single objective function by assigning suitable weights to each objective functions. The main drawback lies in the selection of proper weights. Recently, evolutionary algorithms are used to find the nondominated optimal solutions called as Pareto optimal front in a single run. In recent years, Non-dominated Sorting Genetic Algorithm II (NSGA-II) finds increasing applications in solving multi objective problems comprising of conflicting objectives because of low computational requirements, elitism and parameter-less sharing approach. In this work, we propose a methodology which integrates NSGA-II and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for solving a two bar truss problem. NSGA-II searches for the Pareto set where two bar truss is evaluated in terms of minimizing the weight of the truss and minimizing the total displacement of the joint under the given load. Subsequently, TOPSIS selects the best compromise solution.

RDS-NSGA-II: a memetic algorithm for reference point based multi-objective optimization

2016 ◽  
Vol 49 (5) ◽  
pp. 828-845 ◽  
Author(s):  
Jesus Alejandro Hernández Mejía ◽  
Oliver Schütze ◽  
Oliver Cuate ◽  
Adriana Lara ◽  
Kalyanmoy Deb
Keyword(s):  
Reference Point ◽  
Memetic Algorithm ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective

scholarly journals Classification of Reservoir Recovery Factor for Oil and Gas Reservoirs: A Multi-Objective Feature Selection Approach

2021 ◽  
Vol 9 (8) ◽  
pp. 888
Author(s):  
Qasem Al-Tashi ◽  
Emelia Akashah Patah Akhir ◽  
Said Jadid Abdulkadir ◽  
Seyedali Mirjalili ◽  
Tareq M. Shami ◽  
...  
Keyword(s):  
Feature Selection ◽  
Recovery Factor ◽  
Classification Error ◽  
Grey Wolf Optimizer ◽  
Small Subset ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Conflicting Objectives

The accurate classification of reservoir recovery factor is dampened by irregularities such as noisy and high-dimensional features associated with the reservoir measurements or characterization. These irregularities, especially a larger number of features, make it difficult to perform accurate classification of reservoir recovery factor, as the generated reservoir features are usually heterogeneous. Consequently, it is imperative to select relevant reservoir features while preserving or amplifying reservoir recovery accuracy. This phenomenon can be treated as a multi-objective optimization problem, since there are two conflicting objectives: minimizing the number of measurements and preserving high recovery classification accuracy. In this study, wrapper-based multi-objective feature selection approaches are proposed to estimate the set of Pareto optimal solutions that represents the optimum trade-off between these two objectives. Specifically, three multi-objective optimization algorithms—Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Grey Wolf Optimizer (MOGWO) and Multi-Objective Particle Swarm Optimization (MOPSO)—are investigated in selecting relevant features from the reservoir dataset. To the best of our knowledge, this is the first time multi-objective optimization has been used for reservoir recovery factor classification. The Artificial Neural Network (ANN) classification algorithm is used to evaluate the selected reservoir features. Findings from the experimental results show that the proposed MOGWO-ANN outperforms the other two approaches (MOPSO and NSGA-II) in terms of producing non-dominated solutions with a small subset of features and reduced classification error rate.

scholarly journals Long-Term Hydropower Generation Scheduling of Large-Scale Cascade Reservoirs Using Chaotic Adaptive Multi-Objective Bat Algorithm

Water ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 2373 ◽  
Author(s):  
Lyuwen Su ◽  
Kan Yang ◽  
Hu Hu ◽  
Zhe Yang
Keyword(s):  
Large Scale ◽  
Southern China ◽  
Bat Algorithm ◽  
Economic Benefits ◽  
Population Diversity ◽  
Nsga Ii ◽  
Multi Objective ◽  
Conflicting Objectives ◽  
Logical Mapping ◽  
Hydropower Reservoirs

With growing concerns over renewable energy, the cascade hydropower reservoirs operation (CHRO), which balances the development of economic benefits and power supply security, plays an increasingly important role in hydropower systems. Due to conflicting objectives and complicated operation constraints, the CHRO problem considering the requirements of maximizing power generation benefit and firm power output is determined as a multi-objective optimization problem (MOP). In this paper, a chaotic adaptive multi-objective bat algorithm (CAMOBA) is proposed to solve the CHRO problem, and the external archive set is added to preserve non-dominant solutions. Meanwhile, population initialization based on the improved logical mapping function is adopted to improve population diversity. Furthermore, the self-adaptive local search strategy and mutation operation are designed to escape local minima. The CAMOBA is applied to the CHRO problem of the Qingjiang cascade hydropower stations in southern China. The results show that CAMOBA outperforms the multi-objective bat algorithm (MOBA) and non-dominated sorting genetic algorithms-II (NSGA-II) in different hydrological years. The spacing (SP) and hypervolume (HV) metrics verify the excellent performance of CAMOBA in diversity and convergence. In summary, the CAMOBA is demonstrated to get better scheduling solutions, providing an effective approach for solving the cascade hydropower reservoirs operation (CHRO).

Optimal Photovoltaic System Design with Multi-Objective Optimization

2013 ◽  
Vol 4 (4) ◽  
pp. 63-89 ◽  
Author(s):  
Amin Ibrahim ◽  
Farid Bourennani ◽  
Shahryar Rahnamayan ◽  
Greg F. Naterer
Keyword(s):  
Incident Energy ◽  
State Of The Art ◽  
Photovoltaic System ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Conflicting Objectives ◽  
Generalized Differential ◽  
Maximum Field ◽  
Real World Problems

Recently, several parts of the world suffer from electrical black-outs due to high electrical demands during peak hours. Stationary photovoltaic (PV) collector arrays produce clean and sustainable energy especially during peak hours which are generally day time. In addition, PVs do not emit any waste or emissions, and are silent in operation. The incident energy collected by PVs is mainly dependent on the number of collector rows, distance between collector rows, dimension of collectors, collectors inclination angle and collectors azimuth, which all are involved in the proposed modeling in this article. The objective is to achieve optimal design of a PV farm yielding two conflicting objectives namely maximum field incident energy and minimum of the deployment cost. Two state-of-the-art multi-objective evolutionary algorithms (MOEAs) called Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Generalized Differential Evolution Generation 3 (GDE3) are compared to design PV farms in Toronto, Canada area. The results are presented and discussed to illustrate the advantage of utilizing MOEA in PV farms design and other energy related real-world problems.

Guided Search-Based Multi-Objective Evolutionary Algorithm for Grid Workflow Scheduling

2019 ◽  
pp. 166-195
Author(s):  
Ritu Garg
Keyword(s):  
Performance Metrics ◽  
Scheduling Algorithm ◽  
Economic Cost ◽  
Nsga Ii ◽  
Guided Search ◽  
Multi Objective ◽  
Grid Workflow ◽  
Global Computing ◽  
Search Mechanism ◽  
And Performance

The computational grid provides the global computing infrastructure for users to access the services over a network. However, grid service providers charge users for the services based on their usage and QoS level specified. Therefore, in order to optimize the grid workflow execution, a robust multi-objective scheduling algorithm is needed considering economic cost along with execution performance. Generally, in multi-objective problems, simulations rely on running large number of evaluations to obtain the accurate results. However, algorithms that consider the preferences of decision maker, convergence to optimal tradeoff solutions is faster. Thus, in this chapter, the author proposed the preference-based guided search mechanism into MOEAs. To obtain solutions near the pre-specified regions of interest, the author has considered two MOEAs, namely R-NSGA-II and R-ε-MOEA. Further, to improve the diversity of solutions, a modified form called M-R-NSGA-II is used. Finally, the experimental settings and performance metrics are presented for the evaluation of the algorithms.

Improved hybrid Strength Pareto Evolutionary Algorithms for multi-objective optimization

2018 ◽  
Vol 11 (1) ◽  
pp. 20-46 ◽  
Author(s):  
K. Shankar ◽  
Akshay S. Baviskar
Keyword(s):  
Engineering Design ◽  
Nsga Ii ◽  
Design Problems ◽  
Content Type ◽  
Multi Objective ◽  
Decision Variables ◽  
Engineering Design Problems ◽  
Wide Range ◽  
Conflicting Objectives ◽  
Utopia Point

Purpose The purpose of this paper is to design an improved multi-objective algorithm with better spread and convergence than some current algorithms. The proposed application is for engineering design problems. Design/methodology/approach This study proposes two novel approaches which focus on faster convergence to the Pareto front (PF) while adopting the advantages of Strength Pareto Evolutionary Algorithm-2 (SPEA2) for better spread. In first method, decision variables corresponding to the optima of individual objective functions (Utopia Point) are strategically used to guide the search toward PF. In second method, boundary points of the PF are calculated and their decision variables are seeded to the initial population. Findings The proposed methods are tested with a wide range of constrained and unconstrained multi-objective test functions using standard performance metrics. Performance evaluation demonstrates the superiority of proposed algorithms over well-known existing algorithms (such as NSGA-II and SPEA2) and recent ones such as NSLS and E-NSGA-II in most of the benchmark functions. It is also tested on an engineering design problem and compared with a currently used algorithm. Practical implications The algorithms are intended to be used for practical engineering design problems which have many variables and conflicting objectives. A complex example of Welded Beam has been shown at the end of the paper. Social implications The algorithm would be useful for many design problems and social/industrial problems with conflicting objectives. Originality/value This paper presents two novel hybrid algorithms involving SPEA2 based on: local search; and Utopia point directed search principles. This concept has not been investigated before.

PARETO BASED MULTI-OBJECTIVE OPTIMIZATION OF SOLAR THERMAL ENERGY STORAGE USING GENETIC ALGORITHMS

2010 ◽  
Vol 34 (3-4) ◽  
pp. 463-474 ◽  
Author(s):  
Abolfazl Khalkhali ◽  
Mohamadhosein Sadafi ◽  
Javad Rezapour ◽  
Hamed Safikhani
Keyword(s):  
Genetic Algorithms ◽  
Solar System ◽  
Geometrical Parameters ◽  
Net Energy ◽  
Solar Thermal Energy ◽  
Nsga Ii ◽  
Multi Objective ◽  
Design Variables ◽  
Conflicting Objectives ◽  
Change Material

Net energy stored (Q net) and the discharge time of Phase Change Material (t PCM) in a solar system, are important conflicting objectives to be optimized simultaneously. In the present paper, multi-objective genetic algorithms (GAs) are used for Pareto approach optimization of a solar system using modified NSGA II algorithms. The competing objectives are Q net and t PCM and design variables are some geometrical parameters of solar system. It is shown that some interesting and important relationships as useful optimal design principles involved in the performance of solar system can be discovered. These important results can be used for better design of a solar system.

scholarly journals Evolutionary Multi-Objective Energy Production Optimization: An Empirical Comparison

2020 ◽  
Vol 25 (2) ◽  
pp. 32 ◽  
Author(s):  
Gustavo-Adolfo Vargas-Hákim ◽  
Efrén Mezura-Montes ◽  
Edgar Galván
Keyword(s):  
Pareto Front ◽  
Power Production ◽  
Production Optimization ◽  
Production Costs ◽  
Initial Population ◽  
Nsga Ii ◽  
Power Sources ◽  
Multi Objective ◽  
Conflicting Objectives ◽  
Chaotic Model

This work presents the assessment of the well-known Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and one of its variants to optimize a proposed electric power production system. Such variant implements a chaotic model to generate the initial population, aiming to get a better distributed Pareto front. The considered power system is composed of solar, wind and natural gas power sources, being the first two renewable energies. Three conflicting objectives are considered in the problem: (1) power production, (2) production costs and (3) CO2 emissions. The Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) is also adopted in the comparison so as to enrich the empirical evidence by contrasting the NSGA-II versions against a non-Pareto-based approach. Spacing and Hypervolume are the chosen metrics to compare the performance of the algorithms under study. The obtained results suggest that there is no significant improvement by using the variant of the NSGA-II over the original version. Nonetheless, meaningful performance differences have been found between MOEA/D and the other two algorithms.

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