Application of a multi objective multi-leader particle swarm optimization algorithm on NLP and MINLP problems

Shokrian, Mazdak; High, Karen

doi:10.1016/j.compchemeng.2013.08.004

Cited by 36 publications

(11 citation statements)

References 43 publications

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“…The classic MOEAs are Deb's NSGA-II [19] and Zitzler's SPEA-II [20]. Besides, there are many multi-objective optimization algorithms such as multi-objective PSO [21,22], MODE [23,24], multi-objective gravitational search algorithm (MOGSA) [25,26], multi-objective bat algorithm (MOBA) [27]. Basu [24] introduced the Pareto dominated based selection operator into DE to update new generation and achieved the compromise solutions of hydrothermal dispatch problem with conflicting objectives.…”

Section: Introductionmentioning

confidence: 99%

An extended NSGA-III for solution multi-objective hydro-thermal-wind scheduling considering wind power cost

Yuan

Tian

Yuan

et al. 2015

Energy Conversion and Management

147

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Section: Introductionmentioning

confidence: 99%

An extended NSGA-III for solution multi-objective hydro-thermal-wind scheduling considering wind power cost

Yuan

Tian

Yuan

et al. 2015

Energy Conversion and Management

147

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“…In a survey paper in 2006 on multiobjective particle swarm optimization (MOPSO) [12], it was reported that there were currently over twenty-five different proposals of MOPSO reported in the specialized literature. The studies on MOPSO remain a very active area of research, and the MOPSO has been successfully applied to many practical multiobjective optimization problems, recently applied to robotics [13], industrial management [14], and chemical engineering [15]. It also has been applied in the domain of aerospace including airfoil shape optimization, complex physics/shape optimization, and multidisplinary design optimization [16].…”

Section: Introductionmentioning

confidence: 99%

Asteroid Rendezvous Mission Design Using Multiobjective Particle Swarm Optimization

Luo

Zhou

2014

Mathematical Problems in Engineering

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A new preliminary trajectory design method for asteroid rendezvous mission using multiobjective optimization techniques is proposed. This method can overcome the disadvantages of the widely employed Pork-Chop method. The multiobjective integrated launch window and multi-impulse transfer trajectory design model is formulated, which employes minimum-fuel cost and minimum-time transfer as two objective functions. The multiobjective particle swarm optimization (MOPSO) is employed to locate the Pareto solution. The optimization results of two different asteroid mission designs show that the proposed approach can effectively and efficiently demonstrate the relations among the mission characteristic parameters such as launch time, transfer time, propellant cost, and number of maneuvers, which will provide very useful reference for practical asteroid mission design. Compared with the PCP method, the proposed approach is demonstrated to be able to provide much more easily used results, obtain better propellant-optimal solutions, and have much better efficiency. The MOPSO shows a very competitive performance with respect to the NSGA-II and the SPEA-II; besides a proposed boundary constraint optimization strategy is testified to be able to improve its performance.

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“…For testing the performance of different soft sensor regression models, the five evaluation indicators are used: RMSE, MRE, MAXE, MINE and accuracy, and they are calculated in detail in Eqs. (14)- (18). Where, k is the number of samples, N a is the number of furnaces with absolute error < 5°C, N w is the whole testing times.…”

Section: Molten Steel Temperature Soft Sensor Modelmentioning

confidence: 99%

A New AdaBoost.IR Soft Sensor Method for Robust Operation Optimization of Ladle Furnace Refining

Tian

Yu-dong

et al. 2017

ISIJ International

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LF (Ladle Furnace) refining plays an important role during secondary metallurgic process. The traditional LF refining operation relies on the workers' experience, it is disadvantageous to ensure the stable production, high-quality products and energy saving. A new robust operation optimization method of molten steel temperature based on AdaBoost.IR soft sensor is proposed in LF refining process. Firstly, an intelligent model based on BP (Back Propagation) neural network is established by analyzing the changes of energy during whole refining process of LF as sub intelligent model. Then an AdaBoost.IR is designed for the characters of industrial data, and is suitable for industrial soft sensor modeling. The ensemble soft sensor model is established for realizing the online real time measurement of molten steel temperature with better accuracy by using AdaBoost.IR. Secondly the robust operation optimization model is described by analyzing the process of LF refining based on above AdaBoost.IR soft sensor. And the HPOS-GA is used to solve the optimal operation solution of robust optimization model. The new robust operation optimization of temperature based on AdaBoost.IR is used in 300 t LF of the Baosteel Company. The results of experiments demonstrate the soft sensor can predict the temperature more accuracy and the end temperature of LF refining after robust optimization become more stable.

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Application of a multi objective multi-leader particle swarm optimization algorithm on NLP and MINLP problems

Cited by 36 publications

References 43 publications

An extended NSGA-III for solution multi-objective hydro-thermal-wind scheduling considering wind power cost

An extended NSGA-III for solution multi-objective hydro-thermal-wind scheduling considering wind power cost

Asteroid Rendezvous Mission Design Using Multiobjective Particle Swarm Optimization

A New AdaBoost.IR Soft Sensor Method for Robust Operation Optimization of Ladle Furnace Refining

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