Intelligent Particle Swarm Optimization in Multi-objective Problems

Ho, Shinn-Ying; Ku, Wen-Yuan; Jou, Jun-Wun; Hung, Ming-Hao

doi:10.1007/11731139_92

Cited by 8 publications

(4 citation statements)

References 18 publications

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“…For the iterations where a particle follows its own way, a local search procedure is used. Ho et al [89] proposes a novel intelligent multi-objective particle swarm optimization (IMOPSO) to solve multi-objective optimization problems.…”

Section: Bench Mark Functionsmentioning

confidence: 99%

Swarm Intelligence in Multiple and Many Objectives Optimization: A Survey and Topical Study on EEG Signal Analysis

Mishra

Dehuri

Cho

2015

Multi-Objective Swarm Intelligence

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This paper systematically presents the Swarm Intelligence (SI) methods for optimization of multiple and many objective problems. The fundamental difference of Multiple and Many Objective Optimization problems have been studied very rigorously. The three forefront swarm intelligence methods, i.e., Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Artificial Bee Colony Optimization (ABC) has been deeply studied to understand their ways of solving multiple and many objective problems distinctly. A pragmatic topical study on the behavior of real ants, bird flocks, and honey bees in solving EEG signal analysis completes the survey followed by discussion and extensive number of relevant references.

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Section: Bench Mark Functionsmentioning

confidence: 99%

Swarm Intelligence in Multiple and Many Objectives Optimization: A Survey and Topical Study on EEG Signal Analysis

Mishra

Dehuri

Cho

2015

Multi-Objective Swarm Intelligence

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“…Li [39] introduced a non-dominated sorting PSO method to increase information sharing among all the particles to propel the population to move toward the true Pareto-optimal front. Ho et al [40] used a generalized Pareto-based scaleindependent fitness function for scoring the candidate solutions and employs a divide-and-conquer approach to determine the next move of a particle based on orthogonal experimental design. Tripathi et al [41] presented a time variant MOPSO that allows parameters such as inertia weight and acceleration coefficients to change with iterations.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Cooperative particle swarm optimization for multiobjective transportation planning

Zheng

Chen

2013

Appl Intell

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The paper presents a multiobjective optimization problem that considers distributing multiple kinds of products from multiple sources to multiple targets. The problem is of high complexity and is difficult to solve using classical heuristics. We propose for the problem a hierarchical cooperative optimization approach that decomposes the problem into low-dimensional subcomponents, and applies Pareto-based particle swarm optimization (PSO) method to the main problem and the subproblems alternately. In particular, our approach uses multiple sub-swarms to evolve the sub-solutions concurrently, controls the detrimental effect of variable correlation by reducing the subproblem objectives, and brings together the results of the sub-swarms to construct effective solutions of the original problem. Computational experiment demonstrates that the proposed algorithm is robust and scalable, and outperforms some state-of-the-art constrained multiobjective optimization algorithms on a set of test problems.

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“…Because PSO requires only primitive mathematical operators and is computationally inexpensive in terms of both memory requirements and speed (Parsopoulos and Vrahatis 2002), it has good convergence performance and has been successfully applied in many fields such as neural network training ), integral programming (Kitayama and Yasuda 2006), multi-objective optimization (Ho et al 2006), and decision making (Nenortaitė 2007). …”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Multi-Level Decision Making

Zhang¹,

Lu²,

Gao³

2015

Intelligent Systems Reference Library

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The aim of this series is to publish a Reference Library, including novel advances and developments in all aspects of Intelligent Systems in an easily accessible and well structured form. The series includes reference works, handbooks, compendia, textbooks, well-structured monographs, dictionaries, and encyclopedias. It contains well integrated knowledge and current information in the field of Intelligent Systems. The series covers the theory, applications, and design methods of Intelligent Systems. Virtually all disciplines such as engineering, computer science, avionics, business, e-commerce, environment, healthcare, physics and life science are included.More information about this series at of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. v multi-level decision situations, as well as the use of decision support systems to obtain the desired solutions.• It reflects the latest academic research progress and state-of-the-art development through the results of our own and other authors' recent publications in this field. It does not attempt to provide exhaustive coverage of every fact or research result that exists.This monograph is principally based on our research developments over the past ten years, during which time we have produced more than 50 journal and conference publications in this field.The monograph has 14 chapters, organized into five parts. The first part, from Chaps. 1 to 3, covers concepts of decision-making, decision support systems and bi-level decision-making in general. The second part of the monograph, from Chaps. 4 to 6, presents bi-level multi-follower and tri-level multi-follower decisionmaking, including related models, solution methods, algorithms and case studies. The third part, from Chaps. 7 to 9, focuses on uncertainty issues in multi-level decision-making and discusses fuzzy bi-level and fuzzy multi-objective bi-level decision models and solution algorithms. In Part IV, Chap. 10 deals with a nonprogramming multi-level decision issue and proposes the framework and methods of rule-set-based bi-level decision-making. The last part, from Chaps. 11 to 14, shows the development of bi-level and tri-level decision support systems and related applications by using the methods presented in the previous chapters. These applications include the power market, supply chain management and railway organization.Our potential readers include organizational managers and practicing professionals, who can use the methods and software provided to solve their real decision problems; researchers in the areas of bi-level and multi-level decision-making and decision support systems; students at an advanced undergraduate or master's level in information systems, business administration, or the applicati...

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Intelligent Particle Swarm Optimization in Multi-objective Problems

Cited by 8 publications

References 18 publications

Swarm Intelligence in Multiple and Many Objectives Optimization: A Survey and Topical Study on EEG Signal Analysis

Swarm Intelligence in Multiple and Many Objectives Optimization: A Survey and Topical Study on EEG Signal Analysis

Cooperative particle swarm optimization for multiobjective transportation planning

Multi-Level Decision Making

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