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META-HEURISTICS  February 2013

META-HEURISTICS February 2013

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Subject:

CALL FOR PAPERS and contents of the latest issue IJAMC 3(4)

From:

pyyin <[log in to unmask]>

Reply-To:

pyyin <[log in to unmask]>

Date:

Thu, 14 Feb 2013 15:58:25 +0800

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text/plain

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********************************************************************************
                 CALL FOR PAPERS                
    International Journal of Applied Metaheuristic Computing   
********************************************************************************

Dear Colleagues,

You are courteously invited to submit your research articles to the ¡§International Journal of Applied Metaheuristic Computing¡¨ (http://www.igi-global.com/ijamc). Please send your submissions directly to [log in to unmask]

Topics to be discussed in this journal include (but are not limited to) the following:
&#8226;	Applications of metaheuristic computing
o	Control system, evolvable hardware, power system, vehicular system, communication system, transportation system, and simulation optimization
o	Distributed computing, multimedia processing, machine learning, data mining, bioinformatics, data security, network design, and decision making
o	Government, education, economics, finance, and the others
o	Supply-chain management, production scheduling, resource allocation, human resource planning, strategic planning, project portfolio management, and business process management
&#8226;	Modeling and analysis of metaheuristic computing
o	Ant colony optimization
o	Differential evolution
o	Genetic algorithm
o	GRASP
o	Greedy algorithm
o	Hyper-heuristics
o	Immune algorithm
o	Local search
o	Memetic algorithm
o	Metaheuristics in dynamic, multi-objective, and constrained environment
o	Model-based metaheuristics (matheuristics)
o	Particle swarm optimization
o	Scatter search
o	Simulated annealing
o	Tabu search
o	Variable neighborhood search 
All inquiries and submissions should be sent to: Editor-in-Chief: Peng-Yeng Yin at [log in to unmask]

The contents of the latest issue: Volume 3, Issue 4, October - December 2012

PAPER ONE
Round-Table Group Optimization for Sequencing Problems
Xiao-Feng Xie (The Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA)

In this paper, a round-table group optimization (RTGO) algorithm is presented. RTGO is a simple meta-heuristic framework using the insights of research on group creativity. In a cooperative group, the agents work in iterative sessions to search innovative ideas in a common problem landscape. Each agent has one base idea stored in its individual memory, and one social idea fed by a round-table group support mechanism in each session. The idea combination and improvement processes are respectively realized by using a recombination search (XS) strategy and a local search (LS) strategy, to build on the base and social ideas. RTGO is then implemented for solving two difficult sequencing problems, i.e., the flowshop scheduling problem and the quadratic assignment problem. The domain-specific LS strategies are adopted from existing algorithms, whereas a general XS class, called socially biased combination (SBX), is realized in a modular form. The performance of RTGO is then evaluated on commonly-used benchmark datasets. Good performance on different problems can be achieved by RTGO using appropriate SBX operators. Furthermore, RTGO is able to outperform some existing methods, including methods using the same LS strategies.
To obtain a copy of the entire article, click on the link below.
http://www.igi-global.com/article/round-table-group-optimization-sequencing/74736

To read a PDF sample of this article, click on the link below.
http://www.igi-global.com/viewtitlesample.aspx?id=74736&ptid=59549&t=round-table+group+optimization+for+sequencing+problems

PAPER TWO
Risk Budgeted Portfolio Optimization Using an Extended Ant Colony Optimization Metaheuristic
G. A. Vijayalakshmi Pai (Department of Computer Applications, PSG College of Technology, Coimbatore, India)
Risk Budgeted portfolio optimization problem centering on the twin objectives of maximizing expected portfolio return and minimizing portfolio risk and incorporating the risk budgeting investment strategy, turns complex for direct solving by classical methods triggering the need to look for metaheuristic solutions. This work explores the application of an extended Ant Colony Optimization algorithm that borrows concepts from evolution theory, for the solution of the problem and proceeds to compare the experimental results with those obtained by two other Metaheuristic optimization methods belonging to two different genres viz., Evolution Strategy with Hall of Fame and Differential Evolution, obtained in an earlier investigation. The experimental studies have been undertaken over Bombay Stock Exchange data set (BSE200: July 2001-July 2006) and Tokyo Stock Exchange data set (Nikkei225: July 2001-July 2006). Data Envelopment Analysis has also been undertaken to compare the performance of the technical efficiencies of the optimal risk budgeted portfolios obtained by the three approaches.
To obtain a copy of the entire article, click on the link below.
http://www.igi-global.com/article/risk-budgeted-portfolio-optimization-using/74737

To read a PDF sample of this article, click on the link below.
http://www.igi-global.com/viewtitlesample.aspx?id=74737&ptid=59549&t=risk+budgeted+portfolio+optimization+using+an+extended+ant+colony+optimization+metaheuristic

PAPER THREE
A Filter-and-Fan Metaheuristic for the 0-1 Multidimensional Knapsack Problem
Mahdi Khemakhem (LOGIQ ¡V ISGI, University of Sfax, Sfax, Tunisia), Boukthir Haddar (LOGIQ ¡V ISGI, University of Sfax, Sfax, Tunisia), Khalil Chebil (LOGIQ ¡V ISGI, University of Sfax, Sfax, Tunisia) and Sa&iuml;d Hanafi (LAMIH, University of Valenciennes, Valenciennes, France)
This paper proposes a new hybrid tree search algorithm to the Multidimensional Knapsack Problem (MKP) that effectively combines tabu search with a dynamic and adaptive neighborhood search procedure. The authors¡¦ heuristic, based on a filter-and-fan (F&F) procedure, uses a Linear Programming-based Heuristic to generate a starting solution to the F&F process. A tabu search procedure is used to try to enhance the best solution value provided by the F&F method that generates compound moves by a strategically truncated form of tree search. They report the first application of the F&F method to the MKP. Experimental results obtained on a wide set of benchmark problems clearly demonstrate the competitiveness of the proposed method compared to the state-of-the-art heuristic methods.
To obtain a copy of the entire article, click on the link below.
http://www.igi-global.com/article/filter-fan-metaheuristic-multidimensional-knapsack/74738

To read a PDF sample of this article, click on the link below.
http://www.igi-global.com/viewtitlesample.aspx?id=74738&ptid=59549&t=a+filter-and-fan+metaheuristic+for+the+0-1+multidimensional+knapsack+problem

PAPER FOUR
SODAC: A Simulation-Based Tool for the Optimal Design of Analog Circuits
Amin Sallem (LETI-ENIS, University of Sfax, Sfax, Tunisia), Mourad Fakhfakh (LETI-ENIS, University of Sfax, Sfax, Tunisia), Esteban Tlelo-Cuautle (Department of Electronics, Instituto Nacional de Astrof&iacute;sica, &Oacute;ptica y Electr&oacute;nica, Tonantzintla, Puebla, Mexico) and Mourad Loulou (LETI-ENIS, University of Sfax, Sfax, Tunisia)
It is of common use that analog designers start by optimizing the basic building bloc forming an active circuit in order to ¡¥optimally¡¦ size the latter. Even though it is known a priori that the overall circuit performances will differ from the expected ones, due to the fact that the performances of the basic cells will (considerably) change because of the surrounding circuitry, such approach is very widely used. This is mainly due to the complexity of these ¡¥complex¡¦ circuits. It has recently been shown that the simulation based sizing technique is a very interesting spare solution, since it allows avoiding the (very) ¡¥complex¡¦ modeling task. In this paper the authors propose a simulation based optimizing tool that can handle both mono-objective and multi-objective optimization sizing problems. Viability and benefits of this tool are highlighted through some examples. Obtained results are compared to the ideal expected ones and to the ones that are obtained using the conventional approaches.
To obtain a copy of the entire article, click on the link below.
http://www.igi-global.com/article/sodac-simulation-based-tool-optimal/74739

To read a PDF sample of this article, click on the link below.
http://www.igi-global.com/viewtitlesample.aspx?id=74739&ptid=59549&t=sodac%3a+a+simulation-based+tool+for+the+optimal+design+of+analog+circuits

*****************************************************
For full copies of the above articles, check for this issue of the International Journal of Applied Metaheuristic Computing (IJAMC) in your institution's library. This journal is also included in the IGI Global aggregated "InfoSci-Journals" database: http://www.igi-global.com/eresources/infosci-journals.aspx.
*****************************************************


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