Dynamic multi-objective optimization based on membrane computing for control of time-varying unstable plants

Huang, Liang; Suh, Il Hong; Abraham, Ajith

doi:10.1016/j.ins.2010.12.015

Cited by 136 publications

(67 citation statements)

References 34 publications

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“…Recently, Huang et al [2011] pointed out that all DMOOPs assume that the current found POS does not affect the future POS or POF. To the best knowledge of the authors of this article, none of the suggested DMOOPs have a POS or POF that depends on the previous POS or POF.…”

Section: U(t) and V(t) Functions Of Time T The Selection Of U(t) Andmentioning

confidence: 99%

“…Furthermore, most DMOOPs consist of a static number of decision variables and objective functions. Therefore, Huang et al [2011] introduced four DMOOPs that incorporate these scenarios, defined as follows:…”

Section: U(t) and V(t) Functions Of Time T The Selection Of U(t) Andmentioning

confidence: 99%

“…Numerous real-world DMOOP application areas exist, of which some are hydrothermal power scheduling [Deb et al 2007], machining of gradient material [Roy and Mehnen 2008], controller design for a time-varying unstable plant [Farina et al 2004;Huang et al 2011], war resource allocation [Palaniappan et al 2001], route optimisation according to real-time traffic [Wahle et al 2001], design optimisation of wind turbine structures [Maalawi 2011], supply chain networks [Chen and Lee 2004;Selim et al 2008], and energy-efficient routing optimisation in mobile ad hoc networks [Constantinou 2011].…”

Section: Real-world Dmoo Problemsmentioning

confidence: 99%

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Benchmarks for dynamic multi-objective optimisation algorithms

Helbig

Engelbrecht

2014

ACM Comput. Surv.

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Algorithms that solve Dynamic Multi-Objective Optimisation Problems (DMOOPs) should be tested on benchmark functions to determine whether the algorithm can overcome specific difficulties that can occur in real-world problems. However, for Dynamic Multi-Objective Optimisation (DMOO), no standard benchmark functions are used. A number of DMOOPs have been proposed in recent years. However, no comprehensive overview of DMOOPs exist in the literature. Therefore, choosing which benchmark functions to use is not a trivial task. This article seeks to address this gap in the DMOO literature by providing a comprehensive overview of proposed DMOOPs, and proposing characteristics that an ideal DMOO benchmark function suite should exhibit. In addition, DMOOPs are proposed for each characteristic. Shortcomings of current DMOOPs that do not address certain characteristics of an ideal benchmark suite are highlighted. These identified shortcomings are addressed by proposing new DMOO benchmark functions with complicated Pareto-Optimal Sets (POSs), and approaches to develop DMOOPs with either an isolated or deceptive Pareto-Optimal Front (POF). In addition, DMOO application areas and real-world DMOOPs are discussed.

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Section: U(t) and V(t) Functions Of Time T The Selection Of U(t) Andmentioning

confidence: 99%

Section: U(t) and V(t) Functions Of Time T The Selection Of U(t) Andmentioning

confidence: 99%

Section: Real-world Dmoo Problemsmentioning

confidence: 99%

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Benchmarks for dynamic multi-objective optimisation algorithms

Helbig

Engelbrecht

2014

ACM Comput. Surv.

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“…This membrane structure was also combined with a QIEA and tabu search [32], differential evolution [3], ant colony optimization [29], particle swarm optimization [33] and multiple QIEA components to solve radar emitter signal time-frequency atom decomposition, numerical optimization problems, travelling salesman problems, broadcasting problems in P systems and image processing, respectively. In [11], a dynamic multi-objective optimization algorithm using a membrane system with a hybrid structure was developed to design a controller for a time-varying unstable plant. The dynamic behavior analysis in [31] indicates that the membrane algorithm, QEPS, has a stronger capability to balance exploration and exploitation than its counterpart approach, QIEA.…”

Section: Introductionmentioning

confidence: 99%

QEAM: An Approximate Algorithm Using P Systems with Active Membranes

Zhang

Cheng

Gheorghe

et al. 2015

INT J COMPUT COMMUN

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This paper proposes an approximate optimization approach, called QEAM, which combines a P system with active membranes and a quantum-inspired evolutionary algorithm. QEAM uses the hierarchical arrangement of the compartments and developmental rules of a P system with active membranes, and the objects consisting of quantum-inspired bit individuals, a probabilistic observation and the evolutionary rules designed with quantum-inspired gates to specify the membrane algorithms. A large number of experiments carried out on benchmark instances of satisfiability problem show that QEAM outperforms QEPS (quantum-inspired evolutionary algorithm based on P systems) and its counterpart quantum-inspired evolutionary algorithm.

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“…The research results on membrane computing have made to demonstrate that most of membrane systems are powerful and efficient since they have successfully solved a lot of NP-hard problems in a linear or polynomial time [15][16][17][18][19][20][21]. Motivation of this paper is to develop an effective method under the framework of membrane systems to solve the International Conference on Mechatronics, Electronic, Industrial and Control Engineering (MEIC 2014) optimal multi-level thresholding problem.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Method Based on Tissue Membrane Systems and Velocity-Position Model for Image Thresholding

Yi¹,

Zhang²,

Peng³

2014

Proceedings of the 2014 International Conference on Mechatronics, Electronic, Industrial and Control Engineering

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Abstract-This paper proposes an image thresholding segmentation method, which combines tissue membrane systems and velocity-position model. A tissue membrane system is used as its computing framework and an improved velocity-position model is integrated as evolution rules of objects in cells. Due to parallel computing ability and inherent evolution-communication mechanism of the tissue membrane system, the presented hybrid method can effectively and efficiently find the optimal thresholds for three-level thresholding based on total fuzzy entropy. The performance of the presented hybrid method is studied with several evolutionary algorithms. Simulation results show that the presented hybrid method is superior or comparable to the other evolutionary algorithms and can be efficiently used for image thresholding.

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Dynamic multi-objective optimization based on membrane computing for control of time-varying unstable plants

Cited by 136 publications

References 34 publications

Benchmarks for dynamic multi-objective optimisation algorithms

Benchmarks for dynamic multi-objective optimisation algorithms

QEAM: An Approximate Algorithm Using P Systems with Active Membranes

A Hybrid Method Based on Tissue Membrane Systems and Velocity-Position Model for Image Thresholding

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