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Article
Publication date: 1 February 2002

Mun‐Bo Shim, Myung‐Won Suh, Tomonari Furukawa, Genki Yagawa and Shinobu Yoshimura

In an attempt to solve multiobjective optimization problems, many traditional methods scalarize an objective vector into a single objective by a weight vector. In these…

Abstract

In an attempt to solve multiobjective optimization problems, many traditional methods scalarize an objective vector into a single objective by a weight vector. In these cases, the obtained solution is highly sensitive to the weight vector used in the scalarization process and demands a user to have knowledge about the underlying problem. Moreover, in solving multiobjective problems, designers may be interested in a set of Pareto‐optimal points, instead of a single point. In this paper, Pareto‐based Continuous Evolutionary Algorithms for Multiobjective Optimization problems having continuous search space are introduced. These algorithms are based on Continuous Evolutionary Algorithms, which were developed by the authors to solve single‐objective optimization problems with a continuous function and continuous search space efficiently. For multiobjective optimization, a progressive reproduction operator and a niche‐formation method for fitness sharing and a storing process for elitism are implemented in the algorithm. The operator and the niche formulation allow the solution set to be distributed widely over the Pareto‐optimal tradeoff surface. Finally, the validity of this method has been demonstrated through some numerical examples.

Details

Engineering Computations, vol. 19 no. 1
Type: Research Article
ISSN: 0264-4401

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Article
Publication date: 29 April 2014

Takahiro Sato, Kota Watanabe and Hajime Igarashi

In the development of electromagnetic devices, multiobjective topology optimisation is effective to obtain diverse design candidates for production models. However…

Abstract

Purpose

In the development of electromagnetic devices, multiobjective topology optimisation is effective to obtain diverse design candidates for production models. However, multiobjective topology optimisation has not widely been performed because it is difficult to obtain resultant shapes for engineering realisation due to large search spaces. The purpose of this paper is to present a new multiobjective topology optimisation method.

Design/methodology/approach

This paper presents a new multiobjective topology optimisation method in which the Immune Algorithm is modified for multiobjecrive optimisation and a shape modification process based on spatial filtering is employed.

Findings

The present method shows that better Pareto solutions can be found in comparison with the conventional methods.

Originality/value

A new effective multiobjective topology optimisation is presented. This method enables to diverse design candidates for production models.

Details

COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, vol. 33 no. 3
Type: Research Article
ISSN: 0332-1649

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Article
Publication date: 16 April 2018

Marina Tsili, Eleftherios I. Amoiralis, Jean Vianei Leite, Sinvaldo R. Moreno and Leandro dos Santos Coelho

Real-world applications in engineering and other fields usually involve simultaneous optimization of multiple objectives, which are generally non-commensurable and…

Abstract

Purpose

Real-world applications in engineering and other fields usually involve simultaneous optimization of multiple objectives, which are generally non-commensurable and conflicting with each other. This paper aims to treat the transformer design optimization (TDO) as a multiobjective problem (MOP), to minimize the manufacturing cost and the total owing cost, taking into consideration design constraints.

Design/methodology/approach

To deal with this optimization problem, a new method is proposed that combines the unrestricted population-size evolutionary multiobjective optimization algorithm (UPS-EMOA) with differential evolution, also applying lognormal distribution for tuning the scale factor and the beta distribution to adjust the crossover rate (UPS-DELFBC). The proposed UPS-DELFBC is useful to maintain the adequate diversity in the population and avoid the premature convergence during the generational cycle. Numerical results using UPS-DELFBC applied to the transform design optimization of 160, 400 and 630 kVA are promising in terms of spacing and convergence criteria.

Findings

Numerical results using UPS-DELFBC applied to the transform design optimization of 160, 400 and 630 kVA are promising in terms of spacing and convergence criteria.

Originality/value

This paper develops a promising UPS-DELFBC approach to solve MOPs. The TDO problems for three different transformer specifications, with 160, 400 and 630 kVA, have been addressed in this paper. Optimization results show the potential and efficiency of the UPS-DELFBC to solve multiobjective TDO and to produce multiple Pareto solutions.

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Article
Publication date: 1 September 2005

J. Régnier, B. Sareni and X. Roboam

This paper presents a methodology based on Multiobjective Genetic Algorithms (MOGAs) for the design of electrical engineering systems. MOGAs allow one to optimize multiple…

Abstract

Purpose

This paper presents a methodology based on Multiobjective Genetic Algorithms (MOGAs) for the design of electrical engineering systems. MOGAs allow one to optimize multiple heterogeneous criteria in complex systems, but also simplify couplings and sensitivity analysis by determining the evolution of design variables along the Pareto‐optimal front.

Design/methodology/approach

To illustrate the use of MOGAs in electrical engineering, the optimal design of an electromechanical system has been investigated. A rather simplified case study dealing with the optimal dimensioning of an inverter – permanent magnet motor – reducer – load association is carried out to demonstrate the interest of the approach. The purpose is to simultaneously minimize two objectives: the global losses and the mass of the system. The system model is described by analytical model and we use the MOGA called NSGA‐II.

Findings

From the extraction of Pareto‐optimal solutions, MOGAs facilitate the investigation of parametric sensitivity and the analysis of couplings in the system. Through a simple but typical academic problem dealing with the optimal dimensioning of a inverter – permanent magnet motor – reducer – load association, it has been shown that this multiobjective a posteriori approach could offer interesting outlooks in the global optimization and design of complex heterogeneous systems. The final choice between all Pareto‐optimal configurations can be a posteriori done in relation to other issues which have not been considered in the optimization process. In this paper, we illustrate this point by considering the cogging torque for the final decision.

Originality/value

We have proposed an original quantitative methodology based on correlation coefficients to characterize the system interactions.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, vol. 24 no. 3
Type: Research Article
ISSN: 0332-1649

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Article
Publication date: 17 June 2021

Juan Tamassia Ricco, Rogerio Frauendorf Faria Coimbra and Guilherme Ferreira Gomes

Aircraft wings, one of the most important parts of an aircraft, have seen changes in its topological and design arrangement of both the internal structures and external…

Abstract

Purpose

Aircraft wings, one of the most important parts of an aircraft, have seen changes in its topological and design arrangement of both the internal structures and external shape during the past decades. This study, a numerical, aims to minimize the weight of multilaminate composite aerospace structures using multiobjective optimization.

Design/methodology/approach

The methodology started with the determination of the requirements, both imposed by the certifying authority and those inherent to the light, aerobatic, simple, economic and robust (LASER) project. After defining the requirements, the loads that the aircraft would be subjected to during its operation were defined from the flight envelope considering finite element analysis. The design vector consists of material choice for each laminate of the structure (20 in total), ply number and lay-up sequence (respecting the manufacturing rules) and main spar position to obtain a lightweight and cheap structure, respecting the restrictions of stress, margins of safety, displacements and buckling.

Findings

The results obtained indicated a predominance of the use of carbon fiber. The predominant orientation found on the main spar flange was 0° with its location at 28% of the local chord, in the secondary and main web were ±45°, the skins also had the main orientation at ±45°.

Originality/value

The key innovations in this paper include the evaluation, development and optimization of a laminated composite structure applied to a LASER aircraft wings considering both structural performance and manufacturing costs in multiobjetive optimization. This paper is one of the most advanced investigations performed to composite LASER aircraft.

Details

Aircraft Engineering and Aerospace Technology, vol. 93 no. 6
Type: Research Article
ISSN: 1748-8842

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Article
Publication date: 1 March 1994

I.J. RAMIREZ‐ROSADO and C. ALVAREZ BEL

Classic models for distribution systems design have usually considered only basic aspects such as power capacity limits, power demand requirements and the minimization of…

Abstract

Classic models for distribution systems design have usually considered only basic aspects such as power capacity limits, power demand requirements and the minimization of a single‐objective function that represents the total system expansion cost. However, multiobjective design models include aspects such as reliability evaluations, the optimal voltage profile in the network, social amenity values and geographical conditions of the study area, as well as the basic design aspects. In this paper, a multiobjective model is presented for optimal design of distribution systems by finding the best reliability of the network and the least expensive system expansion simultaneously. A multiobjective method used in applications of the model to practical distribution systems design problems is outlined. The computer results indicate that multiobjective models achieve satisfactory solutions, which consider multiple objectives simultaneously. These solutions, preferred by the planner, are advantageous compared with the classic design solutions.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, vol. 13 no. 3
Type: Research Article
ISSN: 0332-1649

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Article
Publication date: 30 August 2013

Ernesto Benini and Nicola Chiereghin

The purpose of this paper is to present a multi‐objective and multi‐point optimization method to support the preliminary design of an unmixed turbofan mounted on a sample…

Abstract

Purpose

The purpose of this paper is to present a multi‐objective and multi‐point optimization method to support the preliminary design of an unmixed turbofan mounted on a sample UAV/UCAV aircraft.

Design/methodology/approach

An in‐house multi‐objective evolutionary algorithm, a flight simulator and a validated engine simulator are implemented and joined together using object‐oriented programming.

Findings

Optimal values are found of the pressure ratio and corrected mass flow of both the engine fan and compressor as they operate in on/off design conditions (multipoint approach), as well as the engine by‐pass ratio, that contextually minimize time and engine fuel consumption required to cover a fixed trajectory (mission profile). Furthermore, the optimal distribution of the thermodynamic quantities along the trajectory is determined.

Research limitations/implications

The research deals with a preliminary design of an engine, therefore no detailed engine geometry can be found.

Practical implications

The paper shows how a multiobjective and multipoint approach to the design of an engine can affect the choice of the engine architecture. In particular, major practical implications regard how the mission profile can affect the choice of the design point: in fact, there is no longer a definitive design point but the design of a UAV/UCAV should be addressed as a function of the mission profile.

Originality/value

The paper presents a multiobjective and multipoint approach to engine optimization as a function of the mission profile.

Details

Aircraft Engineering and Aerospace Technology, vol. 85 no. 5
Type: Research Article
ISSN: 0002-2667

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Article
Publication date: 18 May 2010

M. Nosratollahi, M. Mortazavi, A. Adami and M. Hosseini

The purpose of this paper is the optimal design of a reentry vehicle configuration to minimize the mission cost which is equal to minimize the heat absorbed (thermal…

Abstract

Purpose

The purpose of this paper is the optimal design of a reentry vehicle configuration to minimize the mission cost which is equal to minimize the heat absorbed (thermal protection system mass) and structural mass and to maximize the drag coefficient (trajectory errors and minimum final velocity).

Design/methodology/approach

There are two optimization approaches for solving this problem: multiobjective optimization (lead to Pareto optimal solutions); and single‐objective optimization (lead to one optimal solution). Single‐objective genetic algorithms (GA) and multiobjective Genetic Algorithms (MOGA) are employed for optimization. In second approach, if there are n objectives (n+1) GA run is needed to find nearest point (optimum point), which leads to increase the time processing. Thus, a modified GA called single run GA (SRGA) is presented as third approach to avoid increasing design time. It means if there are n objectives, just one GA run is enough.

Findings

Two multi module function – Ackley and bump function – are selected for examination the third approach. Results of MOGA, GA and SRGA are presented which show SRGA approach can find the nearest point in much shorter time with acceptable accuracy.

Originality/value

GA, MOGA and SRGA approaches are applied to multidisciplinary design optimization of a reentry vehicle configuration and results show the efficiency of SRGA in complex design optimization problem.

Details

Aircraft Engineering and Aerospace Technology, vol. 82 no. 3
Type: Research Article
ISSN: 0002-2667

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Article
Publication date: 15 November 2011

Piergiorgio Alotto

The purpose of this paper is to show that the performance of differential evolution (DE) can be substantially improved by a combination of techniques. These enhancements…

Abstract

Purpose

The purpose of this paper is to show that the performance of differential evolution (DE) can be substantially improved by a combination of techniques. These enhancements are applicable to both single and multiobjective problems. Their combined use allows the optimization of complex 3D electromagnetic devices.

Design/methodology/approach

DE is improved by a combination of techniques which are applied in a cascade way and their single and combined effect is tested on well‐known benchmarks and domain‐specific applications.

Findings

It is shown that the combined use of enhancement techniques provides substantial improvements in the speed of convergence for both single and multiobjective problems.

Research limitations/implications

The increased speed of convergence may come at the price of a somewhat decreased robustness. However, such behavior is justified by the CPU time constraints under which the optimization has to be performed.

Practical implications

The proposed approach appears to be an efficient general purpose stochastic optimizer for electromagnetic design problems.

Originality/value

This paper explorers the combined use of many of the most recent and successful algorithmic improvements to DE and applies them to both single and multiobjective problems.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, vol. 30 no. 6
Type: Research Article
ISSN: 0332-1649

Keywords

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Article
Publication date: 23 March 2012

Byoung‐Jun Park, Jeoung‐Nae Choi, Wook‐Dong Kim and Sung‐Kwun Oh

The purpose of this paper is to consider the concept of Fuzzy Radial Basis Function Neural Networks with Information Granulation (IG‐FRBFNN) and their optimization

Abstract

Purpose

The purpose of this paper is to consider the concept of Fuzzy Radial Basis Function Neural Networks with Information Granulation (IG‐FRBFNN) and their optimization realized by means of the Multiobjective Particle Swarm Optimization (MOPSO).

Design/methodology/approach

In fuzzy modeling, complexity, interpretability (or simplicity) as well as accuracy of the obtained model are essential design criteria. Since the performance of the IG‐RBFNN model is directly affected by some parameters, such as the fuzzification coefficient used in the FCM, the number of rules and the orders of the polynomials in the consequent parts of the rules, the authors carry out both structural as well as parametric optimization of the network. A multi‐objective Particle Swarm Optimization using Crowding Distance (MOPSO‐CD) as well as O/WLS learning‐based optimization are exploited to carry out the structural and parametric optimization of the model, respectively, while the optimization is of multiobjective character as it is aimed at the simultaneous minimization of complexity and maximization of accuracy.

Findings

The performance of the proposed model is illustrated with the aid of three examples. The proposed optimization method leads to an accurate and highly interpretable fuzzy model.

Originality/value

A MOPSO‐CD as well as O/WLS learning‐based optimization are exploited, respectively, to carry out the structural and parametric optimization of the model. As a result, the proposed methodology is interesting for designing an accurate and highly interpretable fuzzy model.

1 – 10 of 971