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.

An Improved Multi-Objective Particle Swarm Optimization Based on Utopia Point Guided Search

2018 ◽  
Vol 9 (4) ◽  
pp. 71-96 ◽  
Author(s):  
Swapnil Prakash Kapse ◽  
Shankar Krishnapillai
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Mechanical Design ◽  
Particle Swarm ◽  
Nsga Ii ◽  
Swarm Optimization ◽  
Guided Search ◽  
Multi Objective ◽  
Search Approach ◽  
Utopia Point

This article demonstrates the implementation of a novel local search approach based on Utopia point guided search, thus improving the exploration ability of multi- objective Particle Swarm Optimization. This strategy searches for best particles based on the criteria of seeking solutions closer to the Utopia point, thus improving the convergence to the Pareto-optimal front. The elite non-dominated selected particles are stored in an archive and updated at every iteration based on least crowding distance criteria. The leader is chosen among the candidates in the archive using the same guided search. From the simulation results based on many benchmark tests, the new algorithm gives better convergence and diversity when compared to existing several algorithms such as NSGA-II, CMOPSO, SMPSO, PSNS, DE+MOPSO and AMALGAM. Finally, the proposed algorithm is used to solve mechanical design based multi-objective optimization problems from the literature, where it shows the same advantages.

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.

Multi-Objective Evolutionary Algorithms

2016 ◽  
pp. 301-345
Author(s):  
A. K. Nandi ◽  
K. Deb
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Optimization Problem ◽  
Performance Metrics ◽  
Research Work ◽  
Primary Objective ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Mould Material ◽  
Multi Objective

The primary objective in designing appropriate particle reinforced polyurethane composite which will be used as a mould material in soft tooling process is to minimize the cycle time of soft tooling process by providing faster cooling rate during solidification of wax/plastic component. This chapter exemplifies an effective approach to design particle reinforced mould materials by solving the inherent multi-objective optimization problem associated with soft tooling process using evolutionary algorithms. In this chapter, first a brief introduction of multi-objective optimization problem with the key issues is presented. Then, after a short overview on the working procedure of genetic algorithm, a well- established multi-objective evolutionary algorithm, namely NSGA-II along with various performance metrics are described. The inherent multi-objective problem in soft tooling process is demonstrated and subsequently solved using an elitist non-dominated sorting genetic algorithm, NSGA-II. Multi-objective optimization results obtained using NSGA-II are analyzed statistically and validated with real industrial application. Finally the fundamental results of this approach are summarized and various perspectives to the industries along with scopes for future research work are pointed out.

On the Convergence and Diversity of Pareto Fronts Using Swarm Intelligence Metaheuristics for Constrained Search Space

2020 ◽  
pp. 1573-1593
Author(s):  
Kamel Zeltni ◽  
Souham Meshoul ◽  
Heyam H. Al-Baity
Keyword(s):  
Swarm Intelligence ◽  
Performance Metrics ◽  
Optimization Problems ◽  
Cuckoo Search ◽  
Search Space ◽  
Constraint Handling ◽  
Nsga Ii ◽  
Multi Objective ◽  
Handling Mechanism ◽  
Pareto Fronts

This article reviews existing constraint-handling techniques then presents a new design for Swarm Intelligence Metaheuristics (SIM) to deal with constrained multi-objective optimization problems (CMOPs). This new design aims to investigate potential effects of leader concepts that characterize the dynamic of SIM in the hope to help the population to reach Pareto optimal solutions in a constrained search space. The new leader-based constraint handling mechanism is incorporated in Constrained Multi-Objective Cuckoo Search (C-MOCS) and Constrained Multi-Objective Particle Swarm Optimization (C-MOPSO) as specific instances of a more general class of SIMs. The experimental results are carried out using a set of six well-known test functions and two performance metrics. The convergence and diversity of C-MOCS and C-MOPSO are analysed and compared to the well-known Multi-Objective Evolutionary Algorithm (MOEA) NSGA-II and discussed based on the obtained results.

Multi-Objective Evolutionary Algorithms

2017 ◽  
pp. 185-229
Author(s):  
A. K. Nandi ◽  
K. Deb
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Optimization Problem ◽  
Performance Metrics ◽  
Research Work ◽  
Primary Objective ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Mould Material ◽  
Multi Objective

The primary objective in designing appropriate particle reinforced polyurethane composite which will be used as a mould material in soft tooling process is to minimize the cycle time of soft tooling process by providing faster cooling rate during solidification of wax/plastic component. This chapter exemplifies an effective approach to design particle reinforced mould materials by solving the inherent multi-objective optimization problem associated with soft tooling process using evolutionary algorithms. In this chapter, first a brief introduction of multi-objective optimization problem with the key issues is presented. Then, after a short overview on the working procedure of genetic algorithm, a well- established multi-objective evolutionary algorithm, namely NSGA-II along with various performance metrics are described. The inherent multi-objective problem in soft tooling process is demonstrated and subsequently solved using an elitist non-dominated sorting genetic algorithm, NSGA-II. Multi-objective optimization results obtained using NSGA-II are analyzed statistically and validated with real industrial application. Finally the fundamental results of this approach are summarized and various perspectives to the industries along with scopes for future research work are pointed out.

Multi-Objective Optimization of Modified Cycloidal-Toothed Gerotor Pumps by Genetic Algorithm

2019 ◽  
Author(s):  
Andrew J. Robison ◽  
Andrea Vacca
Keyword(s):  
Contact Stress ◽  
Performance Metrics ◽  
Radius Of Curvature ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Flow Ripple ◽  
Multi Objective ◽  
Profile Type ◽  
Radial Gap

Abstract Cycloidal-toothed gerotors are formed by the combination of epicycloid and a hypocyloid arcs that use the pitch circles as their base circles. They are a common profile type used in industry likely because they can be generated by simple parametric equations. One of the problems with the cycloidal-toothed profile type is that the radius of curvature of both the inner and outer gear are zero when the gears contact at the pitch point which can lead to high contact stress. A gear generation algorithm has been developed that modifies the hypocycloid tooth form to eliminate contact in regions with very low radii of curvature that is yet to be described in scientific literature. Seven performance metrics are developed to evaluate size, flow ripple, adhesive wear, contact stress, radial gap leakage, lateral gap leakage, and inlet throttling that are used as objective functions in a multi-objective optimization. The pump geometry is optimized by applying the NSGA-II algorithm with a population size of 1000 for 500 generations to produce a pareto front, and the results are compared to two cycloidal-toothed gerotors used in the automotive industry. Two designs were found that significantly reduce the contact stress in the profiles while giving the same performance in the other six objective functions in comparison to the profiles used in industry. Another two designs are found that can significantly reduce several objective functions if the size of the pump can be increased slightly.

scholarly journals Sustainability Assessment of Constructive Solutions for Urban Spain: A Multi-Objective Combinatorial Optimization Problem

2019 ◽  
Vol 11 (3) ◽  
pp. 839 ◽  
Author(s):  
Simón Martínez ◽  
Cristina González ◽  
Antonio Hospitaler ◽  
Vicente Albero
Keyword(s):  
Genetic Algorithm ◽  
Combinatorial Optimization ◽  
Environmental Impacts ◽  
Co2 Emissions ◽  
Optimization Problem ◽  
Economic Cost ◽  
Sustainability Indicators ◽  
Combinatorial Optimization Problem ◽  
Nsga Ii ◽  
Multi Objective

Industrial areas are set up on plots of roads and associated infrastructure. These use materials and machinery that have environmental impacts, and thus require constructive solutions throughout their lifecycles. In turn, these solutions and their components cause environmental impacts that can be measured by sustainability indicators. The concept of sustainability is closely tied to sustainable development, which is defined as “development that meets the needs of the present, without compromising the ability of future generations to meet their own needs”. The large number of possible and available solutions means that identifying the best one for a given road section must employ a set of heuristic techniques, which conceptualize the issue as a combinatorial optimization problem that is purely discrete and non-differential. The system chosen can be based on a genetic algorithm method that differentiates individuals based on three sustainability indicators: CO2 emissions, embedded energy (also known as embodied energy, defined as the energy expended to manufacture a product), and economic cost. In this paper, we supplement traditional cost analyses using a three-objective multi-objective genetic algorithm that considers the aforementioned criteria, thus addressing sustainability in aggregate planning. The procedure is applied to three objective functions—CO2 emissions, economic cost and embedded energy—for each possible solution. We used the non-dominated sorting genetic algorithm (NSGA-II) to implement multi-objective optimization in MATLAB. Additional results for a random walk and multi-objective search algorithm are shown. This study involved 26 design variables, with different ranks of variation, and the application of the algorithm generates results for the defined Pareto fronts. Our method shows that the optimal approach effectively solves a real-world multi-objective project planning problem, as our solution is one of the Pareto-optimal solutions generated by the NSGA-II.

scholarly journals Design search and optimization in aerospace engineering

2007 ◽  
Vol 365 (1859) ◽  
pp. 2501-2529 ◽  
Author(s):  
A.J Keane ◽  
J.P Scanlan
Keyword(s):  
Performance Metrics ◽  
Computational Modelling ◽  
Problem Formulation ◽  
Aerospace Engineering ◽  
Objective Probability ◽  
Multi Objective ◽  
Competitive Edge ◽  
The Face ◽  
And Performance ◽  
Semi Empirical

In this paper, we take a design-led perspective on the use of computational tools in the aerospace sector. We briefly review the current state-of-the-art in design search and optimization (DSO) as applied to problems from aerospace engineering, focusing on those problems that make heavy use of computational fluid dynamics (CFD). This ranges over issues of representation, optimization problem formulation and computational modelling. We then follow this with a multi-objective, multi-disciplinary example of DSO applied to civil aircraft wing design, an area where this kind of approach is becoming essential for companies to maintain their competitive edge. Our example considers the structure and weight of a transonic civil transport wing, its aerodynamic performance at cruise speed and its manufacturing costs. The goals are low drag and cost while holding weight and structural performance at acceptable levels. The constraints and performance metrics are modelled by a linked series of analysis codes, the most expensive of which is a CFD analysis of the aerodynamics using an Euler code with coupled boundary layer model. Structural strength and weight are assessed using semi-empirical schemes based on typical airframe company practice. Costing is carried out using a newly developed generative approach based on a hierarchical decomposition of the key structural elements of a typical machined and bolted wing-box assembly. To carry out the DSO process in the face of multiple competing goals, a recently developed multi-objective probability of improvement formulation is invoked along with stochastic process response surface models (Krigs). This approach both mitigates the significant run times involved in CFD computation and also provides an elegant way of balancing competing goals while still allowing the deployment of the whole range of single objective optimizers commonly available to design teams.

scholarly journals A multi-objective evolutionary scheme for control points deployment in intelligent transportation systems

2021 ◽  
Vol 11 (2) ◽  
pp. 1641
Author(s):  
Martin Luther Mfenjou ◽  
Ado Adamou Abba Ari ◽  
Arouna Ndam Njoya ◽  
Kolyang Kolyang ◽  
Wahabou Abdou ◽  
...  
Keyword(s):  
Intelligent Transportation Systems ◽  
Optimization Methods ◽  
Transportation Systems ◽  
Control Points ◽  
Nsga Ii ◽  
Multi Objective ◽  
Strength Pareto Evolutionary Algorithm ◽  
Evolutionary Scheme ◽  
And Performance ◽  
Good Coverage

One of the problems that hinder emergency in developing countries is the problem of monitoring a number of activities on inter-urban roadway networks. In the literature, the use of control points is proposed in the context of these countries in order to ensure efficient monitoring, by ensuring a good coverage while minimizing the installation costs as well as the number of accidents across these road networks. In this work, we propose an optimal deployment of these control points from several optimization methods based on some evolutionary multi-objective algorithms: the non-dominated sorting genetic algorithm-II (NSGA-II); the multi-objective particle swarm optimization (MOPSO); the strength Pareto evolutionary algorithm -II (SPEA-II); and the Pareto envelope based selection algorithm-II (PESA-II). We performed the tests and compared these deployments using Pareto front and performance indicators like the spread and hypervolume and the inverted generational distance (IGD). The results obtained show that the NSGA-II method is the most adequate in the deployment of these control points.

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