Solving constrained optimization problems with a hybrid particle swarm optimization algorithm

Cagnina, Leticia; Esquivel, Susana Cecilia; Coello, Carlos A. Coello

doi:10.1080/0305215x.2010.522707

Cited by 103 publications

(137 citation statements)

References 34 publications

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“…Using the proposed flower pollination algorithm, we have easily found the same as the solution f * ≈ 6059.714 obtained by Cagnina et al [2,6].…”

Section: Design Optimizationsupporting

confidence: 53%

“…For a given volume and working pressure, the basic aim of designing a cylindrical vessel is to minimize the total cost. Typically, the design variables are the thickness d 1 of the head, the thickness d 2 of the body, the inner radius r, and the length L of the cylindrical section [2]. This is a well-known test problem for optimization and it can be written as minimize f (x) = 0.6224d 1 rL + 1.7781d 2 r 2 + 3.1661d 2 1 L + 19.84d 2 1 r,…”

Section: Design Optimizationmentioning

confidence: 99%

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Flower Pollination Algorithm for Global Optimization

Yang

2012

Lecture Notes in Computer Science

1,685

970

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Flower pollination is an intriguing process in the natural world. Its evolutionary characteristics can be used to design new optimization algorithms. In this paper, we propose a new algorithm, namely, flower pollination algorithm, inspired by the pollination process of flowers. We first use ten test functions to validate the new algorithm, and compare its performance with genetic algorithms and particle swarm optimization. Our simulation results show the flower algorithm is more efficient than both GA and PSO. We also use the flower algorithm to solve a nonlinear design benchmark, which shows the convergence rate is almost exponential.

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“…Using the proposed flower pollination algorithm, we have easily found the same as the solution f * ≈ 6059.714 obtained by Cagnina et al [2,6].…”

Section: Design Optimizationsupporting

confidence: 53%

Section: Design Optimizationmentioning

confidence: 99%

Flower Pollination Algorithm for Global Optimization

Yang

2012

Lecture Notes in Computer Science

1,685

970

View full text Add to dashboard Cite

show abstract

“…Since then, numerous scientific research has been carried out and the technique has been applied to many diverse problems (Eberhart and Kennedy, 1995;Aziz et al, 2011;Cagnina et al, 2008;Reynolds et al, 2005). PSO results do not improve considerably when the initial number of solution size is substantially large.…”

Section: Review Of Existing Optimisation Strategiesmentioning

confidence: 99%

An Intelligent Novel Tripartite - (PSO-GA-SA) Optimization Strategy

Jackson

Owa

Jackson

2017

IJMHEUR

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This paper presents a tripartite version of particle swarm optimisation, genetic algorithm, and simulated annealing (PSO-GA-SA) optimisation strategy addressing some predominant issues such as the problem of the potential solution being trapped in a local minima solution space, the untimely convergence and the slow rate of arriving at optimal solutions. This strategy is designed with an intelligence beneficiary trade-off between exploration and exploitation of the full potential of all the capabilities of PSO, GA, and SA functioning simultaneously. The design algorithm further incorporates a variable velocity component that introduces random intelligence. There are substantial performance improvements when the novel robust design is first validated with three test functions for the initial case studies. To demonstrate the capabilities to handle complexities and establish scalability in the implementation of the proposed approach, the optimisation strategy is further applied to a high-integrity protection system (HIPS) which is a real-life safety system design optimisation problem with increased number of input variables, constraints, and limitations on the available resources. The novel design performs better than their individual methods using the number of fitness evaluations, as the performance metrics, whilst operating with both a reduced number of generations and initial number of starting potential solutions.

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“…In 2010, Leticia C. , Susana C. and Carlos A. present particle swarm optimization algorithm for solving general constrained optimization problems by allowing the boundary between the feasible and unfeasible regions to be explored because unfeasible solutions play an important role when trying to solve problems with active constraints so, they used shake mechanism when the percentage of unfeasible individuals is higher than 10% .This algorithm provide better diversity maintenance and better exploration of constrained search spaces rather than standard PSO algorithm [14]. In 2011 ,Dervis K. and Bahriye A. modified ABC algorithm by using Deb's rules instead of selection mechanism in order to cope with the constraints and introducing a probabilistic selection that assigns probability values to feasible solutions based on their fitness values and infeasible solutions based on their violations .Results show that improved ABC algorithm is efficiently for solving constraints optimization problems [3].…”

Section: Literature Reviewmentioning

confidence: 99%

An Improved Artificial Bee Colony Algorithm for Linear Least Squares and Constrained Optimization Problems

Taher¹

2017

IJCA

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This paper proposes An Improved Artificial Bee Colony (Deb's-ABC) algorithm for solving constrained optimization problems and Nonnegative linear least squares problems. The proposed approach introduces different methods Based upon new search mechanism to balance exploration and exploitation abilities, generating initial population by using the orthogonal initialization method for achieving initial population that spread regularly over the feasible solution and to enhance the global convergence. In addition, we relax the Deb's rules by replacing the feasible solutions with the approximate feasible solutions in Deb's rules because some infeasible solutions with better objective function value and small violation may carry more important information than some feasible solutions .This algorithm is tested on several benchmark functions. Experimental results compared with a standard ABC and other algorithms show that the proposed algorithm is efficient and competitive algorithm for solving constrained optimization problems.

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Solving constrained optimization problems with a hybrid particle swarm optimization algorithm

Cited by 103 publications

References 34 publications

Flower Pollination Algorithm for Global Optimization

Flower Pollination Algorithm for Global Optimization

An Intelligent Novel Tripartite - (PSO-GA-SA) Optimization Strategy

An Improved Artificial Bee Colony Algorithm for Linear Least Squares and Constrained Optimization Problems

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