Nonlinear continuous multi-response problems: a novel two-phase hybrid genetic based metaheuristic

Fallah-Jamshidi, Saeid; Amiri, Maghsoud; Karimi, N.

doi:10.1016/j.asoc.2010.05.013

Cited by 4 publications

(1 citation statement)

References 31 publications

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“…While it is believed that a higher degree polynomial can further reduce the error, there is a risk as setting a high degree polynomial right at the start may produce an optimisation surface which contains too many local minima. This is dangerous as it may cause the system to terminate at a local minimum rather than a global minimum -a common problem faced by many optimisation algorithms when the ''energy landscape'' is complicated (Fallah-Jamshidi, Arimi, & Karimi, 2010;Klock & Buhmann, 2000). One possible solution is to initiate the optimisation using several different sets of starting values (Venna & Kaski, 2006).…”

Section: Wiener-hammerstein Model Identification Using An Incrementalmentioning

confidence: 98%

Identification of a Wiener–Hammerstein system using an incremental nonlinear optimisation technique

Tan

Wong

Godfrey

2012

Control Engineering Practice

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Section: Wiener-hammerstein Model Identification Using An Incrementalmentioning

confidence: 98%

Identification of a Wiener–Hammerstein system using an incremental nonlinear optimisation technique

Tan

Wong

Godfrey

2012

Control Engineering Practice

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A hybrid algorithm to optimize cutting parameter for machining GFRP composite using alumina cutting tools

Khan¹,

Kumar²,

Poomari³

2011

Int J Adv Manuf Technol

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In this paper, two different evolutionary algorithm-based neural network models were developed to optimise the unit production cost. The hybrid neural network models are, namely, genetic algorithm-based neural network (GA-NN) model and particle swarm optimizationbased neural network (PSO-NN) model. These hybrid neural network models were used to find the optimal cutting conditions of Ti[C,N] mixed alumina-based ceramic cutting tool (CC650) and SiC whisker-reinforced aluminabased ceramic cutting tool (CC670) on machining glass fibre-reinforced plastic (GFRP) composite. The objective considered was the minimization of unit production cost subjected to various machine constraints. An orthogonal design and analysis of variance was employed to determine the effective cutting parameters on the tool life. Neural network helps obtain a fairly accurate prediction, even when enough and adequate information is not available. The GA-NN and PSO-NN models were compared for their performance. Optimal cutting conditions obtained with the PSO-NN model are the best possible compromise compared with the GA-NN model during machining GFRP composite using alumina cutting tool. This model also proved that neural networks are capable of reducing uncertainties related to the optimization and estimation of unit production cost.

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Synergy of ICA and MCDM for multi‐response optimisation problems

Fallah-Jamshidi

Amiri

2013

International Journal of Production Research

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Of late, attempts are being made to optimise production system problems by minimum cost. A good available device in this area is response surface methodology. This methodology combines experimental designs and statistical techniques for empirical model building and optimising. In most situations simulated models for real world problems are non-linear multi-response, while responses are conflicting. The simultaneous optimisation of several conflicting responses is computationally expensive. So this makes the problem solving extremely complex. Since few attempts have been made to scrutinise this domain, in this paper the nonlinear continuous multi-response problem is investigated. In order to tackle multi-response optimisation difficulties, we propose a new hybrid meta heuristic based on the imperialist competitive algorithm. It simulates a socioeconomical procedure, imperialistic competition. When there are some non-dominated solutions in searching space, a technique for order performance by similarity to ideal solution is used to identify which nondominated solutions are imperialists and which ones belong to colonial societies. A particle swarm-like mechanism is employed to model the influence of imperialists on colonies. The algorithm will continue until only one imperialist obtains all countries' possessions. In order to prevent carrying out extensive experiments to find optimum parameters of the algorithm, we apply the Taguchi approach. Since there is no standard benchmark in this field, we use three case studies from distinguished papers in the multi-response optimisation field. Comparing the results with some works mentioned in the literature reveals the superiority of the proposed algorithm.

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Nonlinear continuous multi-response problems: a novel two-phase hybrid genetic based metaheuristic

Cited by 4 publications

References 31 publications

Identification of a Wiener–Hammerstein system using an incremental nonlinear optimisation technique

Identification of a Wiener–Hammerstein system using an incremental nonlinear optimisation technique

A hybrid algorithm to optimize cutting parameter for machining GFRP composite using alumina cutting tools

Synergy of ICA and MCDM for multi‐response optimisation problems

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