Fitting of an Ant Colony approach to Dynamic Optimization through a new set of test functions

Real world problems are often of dynamic nature. They form a class of difficult problems that metaheuristics aim to solve. The goal is not only to attempt to find near-to optimal solutions for a defined objective function, but also to track them in the search space. We will discuss in this article the dynamic optimization in the continuous case. Then we will present the experimentation on a battery of test functions, specially tuned for that purpose, of our ant colony algorithm, DHCIAC (Dynamic Hybrid Continuous Interacting Ant Colony).

[1]  Susana Cecilia Esquivel,et al.  An Evolutionary Algorithm to Track Changes of Optimum Value Locations in Dynamic Environments , 2004 .

[2]  Johann Dréo,et al.  Dynamic Optimization Through Continuous Interacting Ant Colony , 2004, ANTS Workshop.

[3]  Gabriela Ochoa,et al.  Assortative Mating in Genetic Algorithms for Dynamic Problems , 2005, EvoWorkshops.

[4]  Johann Dréo,et al.  Metaheuristics for Hard Optimization: Methods and Case Studies , 2005 .

[5]  André Carlos Ponce de Leon Ferreira de Carvalho,et al.  A genetic algorithm with gene dependent mutation probability for non-stationary optimization problems , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[6]  W. Cedeno,et al.  On the use of niching for dynamic landscapes , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[7]  Luca Maria Gambardella,et al.  Ant colony system: a cooperative learning approach to the traveling salesman problem , 1997, IEEE Trans. Evol. Comput..

[8]  R.W. Morrison,et al.  A test problem generator for non-stationary environments , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[9]  Jürgen Branke,et al.  Proceedings of the Workshop on Evolutionary Algorithms for Dynamic Optimization Problems (EvoDOP-2003) held in conjunction with the Genetic and Evolutionary Computation Conference (GECCO-2003), 12 July 2003, Chicago, USA [online] , 2003 .

[10]  Philippe Collard,et al.  There is ALife beyond convergence: using a dual sharing to adapt in time dependent optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[11]  Arthur Jutan,et al.  Continuous optimization using a dynamic simplex method , 2003 .

[12]  Johann Dréo,et al.  A New Ant Colony Algorithm Using the Heterarchical Concept Aimed at Optimization of Multiminima Continuous Functions , 2002, Ant Algorithms.

[13]  Zbigniew Michalewicz,et al.  Evolutionary optimization in non-stationary environments , 2000 .

[14]  U Aickelin,et al.  Handbook of metaheuristics (International series in operations research and management science) , 2005 .

[15]  Mark Wineberg,et al.  Enhancing the GA's Ability to Cope with Dynamic Environments , 2000, GECCO.

[16]  E. Costa,et al.  USING BIOLOGICAL INSPIRATION TO DEAL WITH DYNAMIC ENVIRONMENTS , 2004 .

[17]  Johann Dréo,et al.  An ant colony algorithm aimed at dynamic continuous optimization , 2006, Appl. Math. Comput..

[18]  Mark Wineberg,et al.  The Shifting Balance Genetic Algorithm: improving the GA in a dynamic environment , 1999 .

[19]  Jrgen Branke Evolutionary approaches to dynamic optimization problems , 2001 .

[20]  Joanne H. Walker,et al.  Combining Evolutionary And Non-evolutionary Methods In Tracking Dynamic Global Optima , 2002, GECCO.

[21]  Claus Bendtsen,et al.  Optimization of Non-Stationary Problems with Evolutionary Algorithms and Dynamic Memory , 2001 .

[22]  T. Krink,et al.  Dynamic memory model for non-stationary optimization , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[23]  Ralf Salomon,et al.  Adaptation on the Evolutionary Time Scale: A Working Hypothesis and Basic Experiments , 1997, Artificial Evolution.

[24]  John J. Grefenstette,et al.  An Approach to Anytime Learning , 1992, ML.

[25]  F. Glover,et al.  Handbook of Metaheuristics , 2019, International Series in Operations Research & Management Science.

[26]  Xiaodong Li,et al.  A particle swarm model for tracking multiple peaks in a dynamic environment using speciation , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[27]  John J. Grefenstette,et al.  Genetic Algorithms for Changing Environments , 1992, PPSN.

[28]  John J. Grefenstette,et al.  Genetic Algorithms for Tracking Changing Environments , 1993, ICGA.