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Experimental Study on Bound Handling Techniques for Multi-objective Particle Swarm Optimization [chapter]

Devang Agarwal, Deepak Sharma
2015 Advances in Intelligent Systems and Computing  
In this paper we formulate several methods for bound handling of decision variables involved in solving a multi-objective optimization problem using particle swarm optimization algorithm.  ...  Many real world optimization scenarios impose certain limitations, in terms of constraints and bounds, on various factors affecting the problem.  ...  Conclusion In this paper we implemented several methods for bound handling of decision variables for multi-objective particle swarm optimization, and showed that MOPSO coupled with boundary handling techniques  ... 
doi:10.1007/978-3-319-28031-8_49 fatcat:22bh7t2ttbaclc4ms2bmqbjahe

Dynamic Optimization with Particle Swarms (DOPS): a meta-heuristic for parameter estimation in biochemical models

Adithya Sagar, Rachel LeCover, Christine Shoemaker, Jeffrey Varner
2018 BMC Systems Biology  
DOPS uses a multi-swarm particle swarm optimization technique to generate candidate solution vectors, the best of which is then greedily updated using dynamically dimensioned search.  ...  Toward these challenges, we developed Dynamic Optimization with Particle Swarms (DOPS), a novel hybrid meta-heuristic that combined multi-swarm particle swarm optimization with dynamically dimensioned  ...  Though deterministic global optimization techniques (for example algorithms based on branch and bound) can handle non-linearity and multi-modality [17, 18] , the absence of derivative information, discontinuous  ... 
doi:10.1186/s12918-018-0610-x pmid:30314484 pmcid:PMC6186122 fatcat:rujvtulmsrhwjpbxfcit6m37ym

Dynamic Optimization with Particle Swarms (DOPS): A meta-heuristic for parameter estimation in biochemical models [article]

Jeffrey Varner, Adithya Sagar, Rachel LeCover, Christine Shoemaker
2017 bioRxiv   pre-print
DOPS uses a multi-swarm particle swarm optimization technique to generate candidate solution vectors, the best of which is then greedily updated using dynamically dimensioned search.  ...  Toward these challenges, we developed Dynamic Optimization with Particle Swarms (DOPS), a novel hybrid meta-heuristic that combined multi-swarm particle swarm optimization with dynamically dimensioned  ...  Though deterministic global optimization techniques (for example algorithms based on branch and bound) can handle non-linearity and multi-modality [17, 18] , the absence of derivative information, discontinuous  ... 
doi:10.1101/240580 fatcat:xnvtl22j75b4jpfv3ocbdhehje

Micro-MOPSO: A Multi-Objective Particle Swarm Optimizer That Uses a Very Small Population Size [chapter]

Juan Carlos Fuentes Cabrera, Carlos A. Coello Coello
2010 Studies in Computational Intelligence  
In this chapter, we present a multi-objective evolutionary algorithm (MOEA) based on the heuristic called "particle swarm optimization" (PSO).  ...  This multi-objective particle swarm optimizer (MOPSO) is characterized for using a very small population size, which allows it to require a very low number of objective function evaluations (only 3000  ...  Particle Swarm Optimizer Micro-MOPSO: A Multi-Objective Particle Swarm Optimizer Micro-MOPSO: A Multi-Objective Particle Swarm Optimizer Micro-MOPSO: A Multi-Objective Particle Swarm Optimizer  ... 
doi:10.1007/978-3-642-05165-4_4 fatcat:z6czwbbnjfenhdnabwkqseinlu

Performance Comparison of Differential Evolution and Particle Swarm Optimization in Constrained Optimization

Mahmud Iwan, R. Akmeliawati, Tarig Faisal, Hayder M.A.A. Al-Assadi
2012 Procedia Engineering  
Particle swarm optimization (PSO) and differential evolution (DE) are among the well-known modern optimization algorithms.  ...  A new constraint handling and stopping criterion technique is also adopted in the optimization algorithm.  ...  On the other hand, another optimization algorithm which is popular due to its simplicity and efficiency is particle swarm optimization (PSO).  ... 
doi:10.1016/j.proeng.2012.07.317 fatcat:mrvkc6fjg5d6jhh6ylkkv3tgva

Economic Emission Short-term Hydrothermal Scheduling using a Dynamically Controlled Particle Swarm Optimization

Vinay K. Jadoun, Nikhil Gupta, K.R. Niazi, Anil Swarnkar
2014 Research Journal of Applied Sciences Engineering and Technology  
In this study a Dynamically Controlled Particle Swarm Optimization (DCPSO) method has been developed to solve Economic Emission Short-Term Hydrothermal Scheduling (EESTHS) problem of power system with  ...  The inertial, cognitive and social behavior of the swarm is modified by introducing exponential functions for better exploration and exploitation of the search space.  ...  This study presents an Efficient method to solve Short Term multi-objective Hydrothermal Scheduling (EESTHS) problem of power systems using a Dynamically Controlled Particle Swarm Optimization (DCPSO)  ... 
doi:10.19026/rjaset.8.1132 fatcat:omrrlnzs55ah7fwv35zf6gfvvu

Optimizing effort and time parameters of COCOMO II estimation using fuzzy multi-objective PSO

Kholed Langsari, Riyanarto Sarno
2017 2017 4th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)  
And we introduced the use of Gaussian Membership Function (GMF) Fuzzy Logic and Multi-Objective Particle Swarm Optimization method (MOPSO) algorithms in calibrating and optimizing the COCOMO II model parameters  ...  In this study, we do investigate the influence of components and attributes to achieve new better accuracy improvement on COCOMO II model.  ...  Multi-Objective Particle Swarm Optimization (MOPSO) 1) Particle Swarm Optimization (PSO). Fig. 2 . 2 Representation of Input LTEX EM using Gaussian Membership Function.  ... 
doi:10.1109/eecsi.2017.8239157 fatcat:kll4j2kuwjfyvmh7fqynjw7zsm

Cooperating swarms: A paradigm for collective intelligence and its application in finance

Sumona Mukhopadhyay, Santo Banerjee
2010 International Journal of Computer Applications  
Studies on parameter estimation for chaotic systems have been investigated recently.  ...  A variant of Particle Swarm Optimization (PSO) known as Chaotic Multi Swarm Particle Swarm Optimization (CMS-PSO) is proposed which is inspired from the metaphor of ecological co-habitation of species.  ...  In effect we are dealing with a dynamical system for multi objective parameter optimization.  ... 
doi:10.5120/1107-1450 fatcat:xcus4ozgfrbuvcpf25qnsbcncq

Particle swarm optimization approach for multi-objective composite box-beam design

S. Suresh, P.B. Sujit, A.K. Rao
2007 Composite structures  
The multi-objective design problem is formulated as a combinatorial optimization problem and solved collectively using particle swarm optimization technique.  ...  The search technique is called particle swarm optimization ('inspired by the choreography of a bird flock').  ...  In this paper, we present a multi-agent search technique called 'Particle Swarm Optimization' (PSO) for solving the multi-objective composite structural design problem.  ... 
doi:10.1016/j.compstruct.2006.10.008 fatcat:3uykfp7mvrd6befht4cxnxudcq

A CMPSO Algorithm Based Approach to Solve the Multi-plant Supply Chain Problem [chapter]

Felix T. S. Chan, Vikas Kumar, Nishikant Mishr
2007 Swarm Intelligence, Focus on Ant and Particle Swarm Optimization  
Particle Swarm Optimization (PSO) algorithm motivated by the flocking of the birds works on the social behavioral interaction among the particles in the swarm.  ...  PSO being one of the emerging computational techniques for optimality has received a lot of attention in recent years.  ...  Swarm Intelligence, Focus on Ant and Particle Swarm Optimization Edited by FelixT.S.  ... 
doi:10.5772/5118 fatcat:o2yni2ca25fmtn6eqzrkxaklma

Application of Grasshopper Optimization Algorithm for Constrained and Unconstrained Test Functions

Abhishek G Neve, Ganesh M Kakandikar, Omkar Kulkarni
2017 International Journal of Swarm Intelligence and Evolutionary Computation  
Penalty method is one of the constraints handling technique. Penalty function: Penalty functions have been a part of the literature on constrained optimization for decades.  ...  Finally the optimization problem can be classified into single objective and multi objective problems depending on the nature of the objective function of the problem [2, 3] .  ... 
doi:10.4172/2090-4908.1000165 fatcat:ld4jcbfqgzdrjibhpweai64mpe

Benefits of Incorporating Designer Preferences Within a Multi-Objective Airfoil Design Framework

Robert Carrese, Hadi Winarto, Jon Watmuff, Upali J. Wickramasinghe
2011 Journal of Aircraft  
We propose a variant of a multi-objective particle swarm optimization algorithm that draws on the domain knowledge of the designer to obtain solutions of interest.  ...  K., and Li, X., “Integrating User Preferences with Particle Swarms for Multi-Objective Optimization,” Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, ACM Press, New York  ... 
doi:10.2514/1.c001009 fatcat:v67r65eab5hrzgmawb5bayspv4

Multi-Objective Particle Swarm Optimization-based Feature Selection for Face Recognition

Souad LARABI-MARIE-SAINTE, Sanaa GHOUZALI
2020 Studies in Informatics and Control  
This paper sets forth a feature selection (FS) method based on speed-constrained multi-objective particle swarm optimization (SMPSO).  ...  as the multi-objective evolutionary algorithm based on decomposition (MOEA/D) and the second non-dominated sorting genetic algorithm (NSGA-II).  ...  Algorithms (MOEA), and Multi-objective Particle Swarm Optimization (MOPSO).  ... 
doi:10.24846/v29i1y202010 fatcat:6hszttzhxfagfcsxkj2b6q5spq

Parameter Settings Optimization in Map Reduce Big Data processing using the MOPSO Algorithm

Lennah Etyang, Lawrence Nderu, Waweru Mwangi
2021 International Journal of Advances in Scientific Research and Engineering  
The algorithm employs the Multi-Objective Particle Swarm Optimization (MOPSO) technique, which uses two objective functions to look for a Pareto optimal solution while optimizing the parameters.  ...  One of the already existing models used for rapid processing together with storage in big data is known as Hadoop MapReduce.  ...  Coleman following his contribution towards constrained minimization functions and the use of the Optimization Toolbox™.  ... 
doi:10.31695/ijasre.2021.33923 fatcat:ttojceuyffcmbhzs777eogxyk4

A parameter-free discrete particle swarm algorithm and its application to multi-objective pavement maintenance schemes

Maher Mahmood, Senthan Mathavan, Mujib Rahman
2018 Swarm and Evolutionary Computation  
In this work, a novel particle swarm algorithm is proposed for a general, discrete, multi-objective problem.  ...  Literature survey tells that particle swarms have not been exploited much, mainly due to unavailability of many techniques in this domain for multi-objective discrete problems like this.  ...  The pre-optimization model was used as an input of a global multi-objective optimization model-based particle swarm optimization (PSO).  ... 
doi:10.1016/j.swevo.2018.03.013 fatcat:vf4skasbjjdztilqalfghzmxi4
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