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An Efficient Hybrid Algorithm with Particle Swarm Optimization and Nelder-Mead Algorithm for Parameter Estimation of Nonlinear Regression Modelling

Aynur YONAR, Harun YONAR
2021 GAZI UNIVERSITY JOURNAL OF SCIENCE  
In this study, an efficient hybrid algorithm, namely PSONM, by combining the exploration capability of Particle Swarm Optimization (PSO) and the exploitation capability of the Nelder-Mead (NM) algorithm  ...  Highlights • An efficient hybrid algorithm is proposed for parameter estimation in nonlinear regression models. • The initial values problem for the parameters in the NM algorithm is solved through PSO  ...  An efficient algorithm is procured as expected by combining the exploration capability of Particle Swarm Optimization (PSO) and the exploitation capability of the Nelder-Mead (NM) algorithm.  ... 
doi:10.35378/gujs.864980 fatcat:u3e27dxlkrbzrnxlufrvkxyace

Particle swarm optimization: Hybridization perspectives and experimental illustrations

Radha Thangaraj, Millie Pant, Ajith Abraham, Pascal Bouvry
2011 Applied Mathematics and Computation  
In the present study an attempt is made to review the hybrid optimization techniques in which one main algorithm is a well known metaheuristic; particle swarm optimization or PSO.  ...  ) and hybrid genetic algorithm particle swarm optimization (GA-PSO) on a test suite of nine conventional benchmark problems.  ...  The second author would like to acknowledge the DST for financial support. Also all the authors are grateful to the constructive comments of the unknown referees.  ... 
doi:10.1016/j.amc.2010.12.053 fatcat:z76dg2yu6zarrltu3kzhjx4a3q

Parameter estimation for a phenomenological model of the cardiac action potential

Fawang Liu, John Walmsley, Kevin Burrage
2011 ANZIAM Journal  
The action potential of a cardiac cell is made up of a complex balance of ionic currents which flow across the cell membrane in response to electrical excitation of the cell.  ...  Biophysically detailed mathematical models of the action potential have grown larger in terms of the variables and parameters required to model new findings in subcellular ionic mechanisms.  ...  Christian Bollensdorff and Maya Bahoshy of the Department of Physiology, Anatomy and Genetics, University of Oxford, UK, for the provision of experimental data for an isolated guinea pig ventricular myocyte  ... 
doi:10.21914/anziamj.v52i0.3812 fatcat:z4jl3kvwkzfmfox4qm4lf6iypa

A parallel hybrid optimization algorithm for fitting interatomic potentials

C. Voglis, P.E. Hadjidoukas, D.G. Papageorgiou, I.E. Lagaris
2013 Applied Soft Computing  
We describe in detail the hybrid global optimization algorithm and various parallel implementation issues.  ...  In this work we present the parallel implementation of a hybrid global optimization algorithm assembled specifically to tackle a class of time consuming interatomic potential fitting problems.  ...  Parallel schemes involving a Particle Swarm Optimization (PSO) [6] variant and the Nelder-Mead Simplex method [7] were presented in [8] .  ... 
doi:10.1016/j.asoc.2013.08.007 fatcat:tu5ijvkr2rai7k2gvy63o7xgve

An Overview of Mutation Strategies in Bat Algorithm

Waqas Haider Bangyal, Jamil Ahmad, Hafiz Tayyab, Sobia Pervaiz
2018 International Journal of Advanced Computer Science and Applications  
Bat algorithm (BA) is a population based stochastic search technique encouraged from the intrinsic manner of bee swarm seeking for their food source.  ...  It is anticipated that this survey would be helpful to study the BA algorithm in detail for the researcher.  ...  In [3] a new variant of BA name Accelerated Bat Algorithm (ABATA) is proposed to improve the local search ability using the Nelder-Mead strategy.  ... 
doi:10.14569/ijacsa.2018.090866 fatcat:usxr4tvhijchxmj2dggnmdwaqi

Classification and Analysis of Optimization Techniques for Integrated Energy Systems Utilizing Renewable Energy Sources: A Review for CHP and CCHP Systems

Mohammad Ali Bagherian, Kamyar Mehranzamir, Amin Beiranvand Pour, Shahabaldin Rezania, Elham Taghavi, Hadi Nabipour-Afrouzi, Mohammad Dalvi-Esfahani, Seyed Morteza Alizadeh
2021 Processes  
Amongst modern heuristic algorithms, each method has contributed more to a certain application; while the Genetic Algorithm (GA) was favored for thermoeconomic optimization, Particle Swarm Optimization  ...  Integrated energy systems make for a very strong proposition since it results in energy saving, fuel diversification, and supply of cleaner energy.  ...  Eberhart were formulating the Particle Swarm Optimization (PSO) algorithm based on the analogy of swarms of birds as they search for food [117] .  ... 
doi:10.3390/pr9020339 fatcat:hjyxm5fkyvdm3m2ojtca7hnmty

Solving constrained optimization problems with a hybrid particle swarm optimization algorithm

Leticia Cecilia Cagnina, Susana Cecilia Esquivel, Carlos A. Coello Coello
2011 Engineering optimization (Print)  
This paper presents a particle swarm optimization algorithm for solving general constrained optimization problems.  ...  The proposed approach introduces different methods to update the particle's information, as well as the use of a double population and a special shake mechanism designed to avoid premature convergence.  ...  Suganthan for providing the source code of DMS-C-PSO as well as Efrén Mezura-Montes and Jorge Isacc Flores-Mendoza for providing the source code of IPSO, both of which were used in the experiments reported  ... 
doi:10.1080/0305215x.2010.522707 fatcat:mroh27zprza4hl3lzjne6fub4u

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Yudong Zhang, Shuihua Wang, Genlin Ji
2015 Mathematical Problems in Engineering  
Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques.  ...  , ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic  ...  to balance the local exploitation and the global exploration of algorithm.  ... 
doi:10.1155/2015/931256 fatcat:ubc5eywnhzdjllxjaywe4czhnm

A review of particle swarm optimization. Part I: background and development

Alec Banks, Jonathan Vincent, Chukwudi Anyakoha
2007 Natural Computing  
Particle Swarm Optimization (PSO), in its present form, has been in existence for roughly a decade, with formative research in related domains (such as social modelling, computer graphics, simulation and  ...  However, in that short period, PSO has gained widespread appeal amongst researchers and has been shown to offer good performance in a variety of application domains, with potential for hybridisation and  ...  PSO offers rapid optimization of complex multidimensional search spaces, and is a popular contemporary algorithm for a wide range of search and optimization problems.  ... 
doi:10.1007/s11047-007-9049-5 fatcat:cy3kpin4rff7riyq26g3nttdrm

Particle Swarm Optimization Combined with Inertia-Free Velocity and Direction Search

Kun Miao, Qian Feng, Wei Kuang
2021 Electronics  
The particle swarm optimization algorithm (PSO) is a widely used swarm-based natural inspired optimization algorithm.  ...  With this hybrid, on the one hand, extensive exploration is still maintained by PSO; on the other hand, the SE is responsible for locating a small region, and then the DS further intensifies the search  ...  Suppose that the search space is D-dimensional, and a swarm consisting of N particles search in it (i.e., the i-th particle is a D-dimensional vector).  ... 
doi:10.3390/electronics10050597 fatcat:z76dze2bmjatbnec4bbxne7olu

SWARM DIRECTIONS EMBEDDED DIFFERENTIAL EVOLUTION FOR FASTER CONVERGENCE OF GLOBAL OPTIMIZATION PROBLEMS

MUSRRAT ALI, MILLIE PANT, AJITH ABRAHAM, CHANG WOOK AHN
2012 International journal on artificial intelligence tools  
In the present study we propose a new hybrid version of Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms called Hybrid DE or HDE for solving continuous global optimization problems  ...  This is done to maintain a balance between the two antagonist factors; exploration and exploitation thereby obtaining a faster convergence.  ...  The particles or members of the swarm fly through a multidimensional search space looking for a potential solution.  ... 
doi:10.1142/s0218213012400131 fatcat:5tgxna3gvnhsjm4iss5dnld7ra

A Review on Artificial Bee Colony Algorithms and Their Applications to Data Clustering

Ajit Kumar, Dharmender Kumar, S. K. Jarial
2017 Cybernetics and Information Technologies  
In the past, many swarm intelligence based techniques for clustering were introduced and proved their performance.  ...  It is a method of creating groups (clusters) of objects, in such a way that objects in one cluster are very similar and objects in different clusters are quite distinct, i.e. intra-cluster distance is  ...  They also proposed to hybridize ABC with Nelder-Mead algorithm and RWDE method to enhance exploration and exploitation capabilities. B a n s a l et al.  ... 
doi:10.1515/cait-2017-0027 fatcat:evxefbetd5gv3enuc7fiulyewm

Adapt-MEMPSODE

Costas Voglis
2013 Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion - GECCO '13 Companion  
MEMPSODE global optimization software tool integrates Particle Swarm Optimization, a prominent population-based stochastic algorithm, with well established efficient local search procedures.  ...  In this work we present an adaptive variant of MEMPSODE where the local search is selected from a predefined pool of different algorithms.  ...  and in The proposed adapt-MEMPSODE algorithm exploits the diversity provided by multiple local searches and achieves better exploration of the search space.  ... 
doi:10.1145/2464576.2466804 dblp:conf/gecco/Voglis13 fatcat:viwbyslajnhzpagkgdz3a7zftu

A comprehensive survey of sine cosine algorithm: variants and applications

Asma Benmessaoud Gabis, Yassine Meraihi, Seyedali Mirjalili, Amar Ramdane-Cherif
2021 Artificial Intelligence Review  
This paper presents a brief overview of the basic SCA and its variants divided into modified, multi-objective, and hybridized versions.  ...  Sine Cosine Algorithm (SCA) is a recent meta-heuristic algorithm inspired by the proprieties of trigonometric sine and cosine functions.  ...  The mathematical formulations of both PSO and NMS were applied to make the search space more effective and ensure a good balance between the exploitation and exploration.  ... 
doi:10.1007/s10462-021-10026-y pmid:34092884 pmcid:PMC8171367 fatcat:moutlepxhvgvbk2arulqyaut6q

Study of Economic Load Dispatch by Various Hybrid Optimization Techniques [chapter]

Dipankar Santra, Arindam Mondal, Anirban Mukherjee
2015 Studies in Computational Intelligence  
Some soft computing techniques like Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), Clonal Selection Algorithm (CSA), Ant Colony Optimization (ACO), Simulated Annealing (SA), Genetic Algorithm  ...  In this book chapter, we focus on the hybrid soft computing approaches in solving ELD problem and present a concise and updated technical review of systems and approaches proposed by different research  ...  Some of these challenges are: (i) Optimal Power Flow (OPF) problem, (ii) optimization of multiple objectives like Reliable Emission and Economic Dispatch (REED) problem and Combined Economic and Emission  ... 
doi:10.1007/978-81-322-2544-7_2 fatcat:ho7thw6wbreilh722rgilol5jy
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