Integrating particle swarm optimization with genetic algorithms for solving nonlinear optimization problems

Abd-El-Wahed, W. F.; Mousa, Abd Allah A.; El-Shorbagy, M. A.

doi:10.1016/j.cam.2010.08.030

Cited by 123 publications

(43 citation statements)

References 7 publications

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“…To evaluate the effectiveness and efficiency of the proposed HPSO-GSA method, extensive simulation studies have been performed for the performance comparison between the HPSO-GSA algorithm and original PSO [57], original GSA [55] and five state-of-the-art PSO variants such as QPSO [37], DPSO [42], FO-DPSO [43], GAPSO [47] and PSOGSA [50] for identifying unknown IIR plants. Five different benchmark plants, which are often used in the reported literatures [19,24,25] and [27], have been taken into consideration in this paper.…”

Section: Description Of Iir System Identification Problems and Paramementioning

confidence: 99%

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A new design method for adaptive IIR system identification using hybrid particle swarm optimization and gravitational search algorithm

Jiang

Wang

2014

Nonlinear Dyn

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Design of adaptive infinite impulse response (IIR) filter is the process of utilizing adaptive algorithm to iteratively determine the filter parameters to obtain an optimal model for the unknown plant based on minimizing the error cost function. However, the error cost surface of IIR filter is generally nonlinear, non-differentiable and multimodal. Hence, an efficient global optimization technique is required to minimize the error cost objective. A novel hybrid particle swarm optimization and gravitational search algorithm (HPSO-GSA) is proposed in this paper for IIR filter design. The proposed HPSO-GSA updates particle positions through obeying the influence of gravity acceleration in GSA and receiving direction of cognitive memory and social sharing information from PSO by means of coevolutionary strategy. The effect of key parameters on the performance of the proposed algorithm is firstly studied, and the proper parameters in HPSO-GSA are established using five benchmark plants along with the same-order model. The simulation studies have been performed for the performance comparison of eight algorithms such as PSO, GSA, QPSO, DPSO, FO-DPSO, GAPSO, PSOGSA and the proposed HPSO-GSA for unknown IIR system identification with the same-order and reduced-order filters. Simulation results show that the proposed algorithm has advantages over PSO, GSA and other PSO-based variants in terms of the convergence speed and the MSE levels.

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Section: Description Of Iir System Identification Problems and Paramementioning

confidence: 99%

“…The most classical hybrid approach that merges GA with PSO, denoted as GAPSO, was proposed for solving nonlinear optimization problems [47]. An evolution of all particles in the algorithm was performed by integrating PSO with GA.…”

Section: Introductionmentioning

confidence: 99%

A new design method for adaptive IIR system identification using hybrid particle swarm optimization and gravitational search algorithm

Jiang

Wang

2014

Nonlinear Dyn

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“…The proposed algorithm have been implemented in MATLAB environment. We have kept the proposed approach parameters same in all problems as is shown in Table 1 (see [35,38]). The power system is interconnected by 41 transmission lines and the total system demand for the 21 load buses is 2.834 p.u.…”

Section: Implementation Of the Proposed Approachmentioning

confidence: 99%

“…In this stage a homogeneous PSO for multi-objective optimization problem (see [34]) is proposed with a decreasing constriction factor to restrict velocity of the particles and control it [35][36][37]. In homogeneous PSO one global repository concept is proposed for choosing pbest and gbest, this means that each particle has lost its own identity and treated simply as a member of social group.…”

Section: Pso Stagementioning

confidence: 99%

Reference Point Based TR-PSO for Multi-Objective Environmental/Economic Dispatch

El‐Sawy¹,

Hendawy²,

El-Shorbagy³

2013

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A reference point based multi-objective optimization using a combination between trust region (TR) algorithm and particle swarm optimization (PSO) to solve the multi-objective environmental/economic dispatch (EED) problem is presented in this paper. The EED problem is handled by Reference Point Interactive Approach. One of the main advantages of the proposed approach is integrating the merits of both TR and PSO, where TR has provided the initial set (close to the Pareto set as possible and the reference point of the decision maker) followed by PSO to improve the quality of the solutions and get all the points on the Pareto frontier. The performance of the proposed algorithm is tested on standard IEEE 30-bus 6-genrator test system and is compared with conventional methods. The results demonstrate the capabilities of the proposed approach to generate true and well-distributed Pareto-optimal non-dominated solutions in one single run. The comparison with the classical methods demonstrates the superiority of the proposed approach and confirms its potential to solve the multi-objective EED problem.

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“…In this paper, we analyze several different optimization problems in reinsurance that can be treated as a BBOP and solve them by using two state-of-the-art evolutionary and swarm intelligence approaches: the evolutionary programming and particle swarm optimization algorithms [12]. Specifically, we provide a discussion based on three optimization problems related to reinsurance contracts, which may affect the solvency of the insurer and reinsurer.…”

Section: Introductionmentioning

confidence: 99%

Effectively Tackling Reinsurance Problems by Using Evolutionary and Swarm Intelligence Algorithms

Salcedo‐Sanz

Carro-Calvo

Bielsa

et al. 2014

Risks

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This paper is focused on solving different hard optimization problems that arise in the field of insurance and, more specifically, in reinsurance problems. In this area, the complexity of the models and assumptions considered in the definition of the reinsurance rules and conditions produces hard black-box optimization problems (problems in which the objective function does not have an algebraic expression, but it is the output of a system (usually a computer program)), which must be solved in order to obtain the optimal output of the reinsurance. The application of traditional optimization approaches is not possible in this kind of mathematical problem, so new computational paradigms must be applied to solve these problems. In this paper, we show the performance of two evolutionary and swarm intelligence techniques (evolutionary programming and particle swarm optimization). We provide an analysis in three black-box optimization problems in reinsurance, where the proposed approaches exhibit an excellent behavior, finding the optimal solution within a fraction of the computational cost used by inspection or enumeration methods.

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Integrating particle swarm optimization with genetic algorithms for solving nonlinear optimization problems

Cited by 123 publications

References 7 publications

A new design method for adaptive IIR system identification using hybrid particle swarm optimization and gravitational search algorithm

A new design method for adaptive IIR system identification using hybrid particle swarm optimization and gravitational search algorithm

Reference Point Based TR-PSO for Multi-Objective Environmental/Economic Dispatch

Effectively Tackling Reinsurance Problems by Using Evolutionary and Swarm Intelligence Algorithms

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