CIlib: A collaborative framework for Computational Intelligence algorithms - Part I

Research in computational intelligence (CI) has produced a huge collection of algorithms, grouped into the main CI paradigms. Development of a new CI algorithm requires such algorithm to be thoroughly benchmarked against existing algorithms, which requires researchers to implement already published algorithms. This re-implementation of existing algorithms unnecessarily wastes valuable time, and may be the cause of incorrect results due to unexpected bugs in the code. It is also the case that more, new CI algorithms are hybrids of algorithms from different paradigms. This illustrates a demand for a comprehensive library of CI algorithms, to minimize development time and the occurrence of programming errors, and to facilitate combination of components to form hybrid models. This paper presents such a library, called CIlib.

[1]  R. Storn,et al.  Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series) , 2005 .

[2]  Frans van den Bergh,et al.  A NICHING PARTICLE SWARM OPTIMIZER , 2002 .

[3]  Andries Petrus Engelbrecht,et al.  CIlib: A collaborative framework for Computational Intelligence algorithms - Part II , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[4]  Russell C. Eberhart,et al.  A discrete binary version of the particle swarm algorithm , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[5]  Rolf Wanka,et al.  Particle Swarm Optimization in High-Dimensional Bounded Search Spaces , 2007, 2007 IEEE Swarm Intelligence Symposium.

[6]  James Kennedy,et al.  Bare bones particle swarms , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[7]  Van der Stockt,et al.  A Generic Neural Network Framework Using Design Patterns , 2008 .

[8]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[9]  Andries Petrus Engelbrecht,et al.  Fundamentals of Computational Swarm Intelligence , 2005 .

[10]  Andries Petrus Engelbrecht,et al.  CiClops: computational intelligence collaborative laboratory of pantological software , 2005, Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. SIS 2005..

[11]  Konstantinos E. Parsopoulos,et al.  MULTIOBJECTIVE OPTIMIZATION USING PARALLEL VECTOR EVALUATED PARTICLE SWARM OPTIMIZATION , 2003 .

[12]  Hartmut Schmeck,et al.  Designing evolutionary algorithms for dynamic optimization problems , 2003 .

[13]  J. Kennedy,et al.  Population structure and particle swarm performance , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[14]  Ralph Johnson,et al.  design patterns elements of reusable object oriented software , 2019 .

[15]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[16]  Hans-Paul Schwefel,et al.  Numerical Optimization of Computer Models , 1982 .

[17]  Tim Hendtlass,et al.  Preserving Diversity in Particle Swarm Optimisation , 2003, IEA/AIE.

[18]  J. Snyman A new and dynamic method for unconstrained minimization , 1982 .

[19]  John Fulcher,et al.  Computational Intelligence: An Introduction , 2008, Computational Intelligence: A Compendium.

[20]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[21]  Edwin S. Peer,et al.  A Serendipitous Software Framework for Facilitating Collaboration in Computational Intelligence , 2004 .

[22]  A. Engelbrecht,et al.  A new locally convergent particle swarm optimiser , 2002, IEEE International Conference on Systems, Man and Cybernetics.

[23]  Kalyan Veeramachaneni,et al.  Fitness-distance-ratio based particle swarm optimization , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[24]  Russell C. Eberhart,et al.  Computational intelligence - concepts to implementations , 2007 .

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

[26]  N. Franken,et al.  Combining particle swarm optimisation with angle modulation to solve binary problems , 2005, 2005 IEEE Congress on Evolutionary Computation.

[27]  Jan Peters,et al.  Computational Intelligence: Principles, Techniques and Applications , 2007, Comput. J..

[28]  John Hallam,et al.  IEEE International Joint Conference on Neural Networks , 2005 .

[29]  Peter J. Bentley,et al.  Dynamic Search With Charged Swarms , 2002, GECCO.

[30]  Jürgen Branke,et al.  Multi-swarm Optimization in Dynamic Environments , 2004, EvoWorkshops.

[31]  Andries Petrus Engelbrecht,et al.  A Cooperative approach to particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[32]  Zong Woo Geem,et al.  A New Heuristic Optimization Algorithm: Harmony Search , 2001, Simul..

[33]  Xiao-Feng Xie,et al.  Handling boundary constraints for numerical optimization by particle swarm flying in periodic search space , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[34]  Andries Petrus Engelbrecht,et al.  Bare bones differential evolution , 2009, Eur. J. Oper. Res..

[35]  J. Kennedy,et al.  Neighborhood topologies in fully informed and best-of-neighborhood particle swarms , 2003, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[36]  Richard K. Belew,et al.  New Methods for Competitive Coevolution , 1997, Evolutionary Computation.