A Shared-Memory ACO-Based Algorithm for Numerical Optimization

Numerical optimization techniques are applied to a variety of engineering problems. The objective function evaluation is an important part of the numerical optimization and is usually realized as a black-box simulator. For efficient solving the numerical optimization problem, new shared-memory approach is proposed. The algorithm is based on an ACO meta-heuristics, where indirect coordination between ants drives the search procedure towards the optimal solution. Indirect coordination offers a high degree of parallelism and therefore relatively straightforward shared-memory implementation. For the communication between processors, the Intel-OpenMP library is used. It is shown that speed-up strongly depends on the simulation time. Therefore, algorithm's performance, according to simulator's time complexity, is experimentally evaluated and discussed.

[1]  Marc Gravel,et al.  Comparing Parallelization of an ACO: Message Passing vs. Shared Memory , 2005, Hybrid Metaheuristics.

[2]  Barbara Chapman,et al.  Using OpenMP - portable shared memory parallel programming , 2007, Scientific and engineering computation.

[3]  Jurij Silc,et al.  A Distributed Multilevel Ant Colonies Approach , 2008, Informatica.

[4]  E.-G. Talbia,et al.  Parallel Ant Colonies for the quadratic assignment problem , 2001, Future Gener. Comput. Syst..

[5]  Ian C. Parmee,et al.  The Ant Colony Metaphor for Searching Continuous Design Spaces , 1995, Evolutionary Computing, AISB Workshop.

[6]  Marco Dorigo,et al.  Ant algorithms and stigmergy , 2000, Future Gener. Comput. Syst..

[7]  Jurij Silc,et al.  The differential Ant-Stigmergy Algorithm for large-scale global optimization , 2010, IEEE Congress on Evolutionary Computation.

[8]  Jurij Silc,et al.  The Differential Ant-Stigmergy Algorithm applied to dynamic optimization problems , 2009, 2009 IEEE Congress on Evolutionary Computation.

[9]  Jun Zhang,et al.  Pseudo Parallel Ant Colony Optimization for Continuous Functions , 2007, Third International Conference on Natural Computation (ICNC 2007).

[10]  Andrew Lewis,et al.  A Parallel Implementation of Ant Colony Optimization , 2002, J. Parallel Distributed Comput..

[11]  Kalyanmoy Deb,et al.  A Computationally Efficient Evolutionary Algorithm for Real-Parameter Optimization , 2002, Evolutionary Computation.

[12]  Thomas Stützle,et al.  Parallelization Strategies for Ant Colony Optimization , 1998, PPSN.

[13]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[14]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[15]  Jurij Silc,et al.  A distributed ant-based algorithm for numerical optimization , 2009, BADS '09.

[16]  Bogdan Filipic,et al.  The differential ant-stigmergy algorithm , 2012, Inf. Sci..

[17]  Vincenzo Cutello,et al.  An Immunological Algorithm for Global Numerical Optimization , 2005, Artificial Evolution.

[18]  Alden H. Wright,et al.  Genetic Algorithms for Real Parameter Optimization , 1990, FOGA.

[19]  Christian Blum,et al.  Ant colony optimization: Introduction and recent trends , 2005 .