A hybrid genetic algorithm and particle swarm optimization for multimodal functions

Kao, Yu-Cheng; Zahara, Erwie

doi:10.1016/j.asoc.2007.07.002

Cited by 472 publications

(198 citation statements)

References 12 publications

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“…7 and 8 and Table 4 demonstrates that the Hybrid optimiser is slightly more accurate in the THC light-off curve, whereas the nPSO algorithm matches the NO peak with a slightly greater accuracy. The best input variables reported by both these optimisers, Table 5, are similar to the input objective variables, indicating that the best solutions found are within the area of the global optima, any difference being attributed to the known convergence issues of these optimisers [12,34].…”

Section: Doc Aftertreatment System-resultsmentioning

confidence: 61%

“…It is based on the work presented by Kao et al who reported that this algorithm outperformed a continuous GA over a number of mathematical functions [34]. This algorithm utilises the search process from both GA and PSO algorithms.…”

Section: Ga-pso Hybrid Model (Hybrid)mentioning

confidence: 99%

“…Finally, a non-uniform mutation rate increases the chance of mutation as a function of number of iterations. This is an important consideration as the mutation search function becomes the main search function for GA as the system starts to converge [25,34].…”

Section: Ga-pso Hybrid Model (Hybrid)mentioning

confidence: 99%

See 2 more Smart Citations

The Development and Application of an Evolutionary Algorithm for the Determination of Kinetic Parameters in Automotive Aftertreatment Models

Pedlow

McCullough

Goguet

et al. 2018

Emiss. Control Sci. Technol.

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Accurate mathematical models are an essential tool in the development of aftertreatment systems, as they can provide detailed information on the impact of design changes while simultaneously reducing development costs and the time between the initiation and completion of a catalytic process development. Identifying the set of kinetic parameters that achieves a perceived acceptable level of accuracy may require significant time and effort. Optimisation techniques can be used to speed up the tuning process; however, these techniques can require a large computation time, and may not produce a satisfactory answer. This invariably leads to questioning regarding the accuracy of such automated approaches. In this paper, the performance of a number of optimisation algorithms including a GA, nPSO and a hybrid algorithm has been explored with respect to their applicability to kinetic parameters optimisation in the context of aftertreatment modelling. The focus is therefore on the assessment of the optimisers' performance, rather than the characterisation of catalytic reactions. The different algorithms were applied to the optimisation of parameters in a number of mathematical functions and theoretical aftertreatment systems. The optimisation algorithms were tested on theoretical aftertreatment systems since these have known absolute solutions thereby allowing the optimisers' performance to be assessed in the absence of any other external source of inaccuracy such as model structure and experimental error. The results obtained demonstrate that such optimisation approaches facilitate the determination of kinetic parameters with suitable accuracy. The proposed hybrid optimisation algorithm achieved excellent performance in considerably shorter computation time than the GA or nPSO optimisers.

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Section: Doc Aftertreatment System-resultsmentioning

confidence: 61%

Section: Ga-pso Hybrid Model (Hybrid)mentioning

confidence: 99%

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The Development and Application of an Evolutionary Algorithm for the Determination of Kinetic Parameters in Automotive Aftertreatment Models

Pedlow

McCullough

Goguet

et al. 2018

Emiss. Control Sci. Technol.

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“…MOIMPSO clustering algorithm is a hybrid of multi-objective clustering algorithm and PSO that was presented to obtain a single best solution from the Pareto optimal archive [35]. By combining two genetic and PSO algorithms, Kao et al invented a new method in which it has benefitted from jump and junction operator for genetic [22]. This approach could solve different problems of continual functions.…”

Section: Related Workmentioning

confidence: 99%

An Optimized K-Harmonic Means Algorithm Combined with Modified Particle Swarm Optimization and Cuckoo Search Algorithm

Bouyer

2016

Foundations of Computing and Decision Sciences

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Abstract. Among the data clustering algorithms, k-means (KM) algorithm is one of the most popular clustering techniques due to its simplicity and efficiency. However, k-means is sensitive to initial centers and it has the local optima problem. K-harmonic-means (KHM) clustering algorithm solves the initialization problem of k-means algorithm, but it also has local optima problem. In this paper, we develop a new algorithm for solving this problem based on an improved version of particle swarm optimization (IPSO) algorithm and KHM clustering. In the proposed algorithm, IPSO is equipped with Cuckoo Search algorithm and two new concepts used in PSO in order to improve the efficiency, fast convergence and escape from local optima. IPSO updates positions of particles based on a combination of global worst, global best with personal worst and personal best to dynamically be used in each iteration of the IPSO. The experimental result on five real-world datasets and two artificial datasets confirms that this improved version is superior to k-harmonic means and regular PSO algorithm. The results of the simulation show that the new algorithm is able to create promising solutions with fast convergence, high accuracy and correctness while markedly improving the processing time.

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“…Unlike GAs, BPSO does not contain any crossover and mutation processes [12]. Hybridization of evolutionary algorithms with local search has been investigated in many studies [13], [14]. Such hybrids are often referred to as memetic algorithms (MA).…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Evolutionary Optimization of PID Controller by Chaotic Particle Swarm Optimization

Gholipour¹,

Addeh²,

Mojallali³

et al. 2012

IJCEE

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A hybrid genetic algorithm and particle swarm optimization for multimodal functions

Cited by 472 publications

References 12 publications

The Development and Application of an Evolutionary Algorithm for the Determination of Kinetic Parameters in Automotive Aftertreatment Models

The Development and Application of an Evolutionary Algorithm for the Determination of Kinetic Parameters in Automotive Aftertreatment Models

An Optimized K-Harmonic Means Algorithm Combined with Modified Particle Swarm Optimization and Cuckoo Search Algorithm

Multi-Objective Evolutionary Optimization of PID Controller by Chaotic Particle Swarm Optimization

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