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What is Multiobjective Optimization

Chapter 20
Handbook of Research on Modern Optimization Algorithms and Applications in Engineering and Economics
Optimization problems which are represented with more than one objective functions.
Published in Chapter:
Swarm Intelligence for Multiobjective Optimization of Extraction Process
T. Ganesan (The University of Adelaide, Australia), I. Elamvazuthi (Universiti Technologi PETRONAS, Malaysia), and P. Vasant (Universiti Technologi PETRONAS, Malaysia)
Copyright: © 2016 |Pages: 29
DOI: 10.4018/978-1-4666-9644-0.ch020
Abstract
Multi objective (MO) optimization is an emerging field which is increasingly being implemented in many industries globally. In this work, the MO optimization of the extraction process of bioactive compounds from the Gardenia Jasminoides Ellis fruit was solved. Three swarm-based algorithms have been applied in conjunction with normal-boundary intersection (NBI) method to solve this MO problem. The gravitational search algorithm (GSA) and the particle swarm optimization (PSO) technique were implemented in this work. In addition, a novel Hopfield-enhanced particle swarm optimization was developed and applied to the extraction problem. By measuring the levels of dominance, the optimality of the approximate Pareto frontiers produced by all the algorithms were gauged and compared. Besides, by measuring the levels of convergence of the frontier, some understanding regarding the structure of the objective space in terms of its relation to the level of frontier dominance is uncovered. Detail comparative studies were conducted on all the algorithms employed and developed in this work.
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Chapter 5
Multilevel Image Segmentation by a Multiobjective Genetic Algorithm Based OptiMUSIG Activation Function
A method to determine one or more optimum solutions from an optimization problem that have multiple objective functions.
Published in Chapter: Multilevel Image Segmentation by a Multiobjective Genetic Algorithm Based OptiMUSIG Activation Function; From: Handbook of Research on Computational Intelligence for Engineering, Science, and Business
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Chapter 11
Multilevel and Color Image Segmentation by NSGA II Based OptiMUSIG Activation Function
A method to determine one or more optimum solutions from an optimization problem that have multiple objective functions.
Published in Chapter: Multilevel and Color Image Segmentation by NSGA II Based OptiMUSIG Activation Function; From: Handbook of Research on Advanced Hybrid Intelligent Techniques and Applications
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Chapter 23
Multiobjective Strategy for an Industrial Gas Turbine: Absorption Chiller System
A class of optimization problems formulated with more than one objective functions.
Published in Chapter: Multiobjective Strategy for an Industrial Gas Turbine: Absorption Chiller System; From: Handbook of Research on Emergent Applications of Optimization Algorithms
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Chapter 15
Multi-Objective Generation Scheduling Using Genetic-Based Fuzzy Mathematical Programming Technique
Multiobjective optimization (MOO) instead of combining all the objectives to single objective, objectives are solved simultaneously and hence retain the significance of each objective. MOO has been available for about two decades, and its application in real world problems is continuously increasing. In a Multiobjective optimization environment the main challenge is to minimize the distance of the generated solutions to the Pareto set and to maximize the diversity of the developed Pareto set. A good Pareto set may be obtained by appropriate guiding of the search process through careful design of reproduction operators and fitness assignment strategies. The objective of Multiobjective is to determine the best approximation to this Pareto optimal set.
Published in Chapter: Multi-Objective Generation Scheduling Using Genetic-Based Fuzzy Mathematical Programming Technique; From: Handbook of Research on Novel Soft Computing Intelligent Algorithms: Theory and Practical Applications
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