History-driven firefly algorithm for optimisation in dynamic and uncertain environments

Due to dynamic and uncertain nature of many optimisation problems in real-world, the applied algorithm in this environment must be able to continuously track the changing optima over time. In this paper, we report a novel speciation-based firefly algorithm for dynamic optimisation, which improved its performance by employing prior landscape historical information. The proposed algorithm, namely history-driven speciation-based firefly algorithm HdSFA, uses a binary space partitioning BSP tree to capture the important information about the landscape during the optimisation process. By utilising this tree, the algorithm can approximate the fitness landscape and avoid wasting the fitness evaluation for some unsuitable solutions. The proposed algorithm is evaluated on the most well-known dynamic benchmark problem, moving peaks benchmark MPB, and also on a modified version of it, called MPB with pendulum-like motion among the environments PMPB, and its performance is compared with that of several state-of-the-art algorithms in the literature. The experimental results and statistical test prove that HdSFA outperforms most of the algorithms in different scenarios.

[1]  Xiao Zhi Gao,et al.  An immune-based ant colony algorithm for static and dynamic optimization , 2007, 2007 IEEE International Conference on Systems, Man and Cybernetics.

[2]  Changhe Li,et al.  A clustering particle swarm optimizer for dynamic optimization , 2009, 2009 IEEE Congress on Evolutionary Computation.

[3]  Changhe Li,et al.  A Clustering Particle Swarm Optimizer for Locating and Tracking Multiple Optima in Dynamic Environments , 2010, IEEE Transactions on Evolutionary Computation.

[4]  Shiu Yin Yuen,et al.  A Genetic Algorithm That Adaptively Mutates and Never Revisits , 2009, IEEE Trans. Evol. Comput..

[5]  Bin Li,et al.  Multi-strategy ensemble particle swarm optimization for dynamic optimization , 2008, Inf. Sci..

[6]  Shiu Yin Yuen,et al.  A non-revisiting Genetic Algorithm , 2007, 2007 IEEE Congress on Evolutionary Computation.

[7]  Janez Brest,et al.  A comprehensive review of firefly algorithms , 2013, Swarm Evol. Comput..

[8]  Ming Yang,et al.  An Adaptive Multi-Swarm Optimizer for Dynamic Optimization Problems , 2014, Evolutionary Computation.

[9]  Mohammad Reza Meybodi,et al.  A hibernating multi-swarm optimization algorithm for dynamic environments , 2010, 2010 Second World Congress on Nature and Biologically Inspired Computing (NaBIC).

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

[11]  Ponnuthurai N. Suganthan,et al.  Evolutionary programming with ensemble of explicit memories for dynamic optimization , 2009, 2009 IEEE Congress on Evolutionary Computation.

[12]  Mohammad Reza Meybodi,et al.  A new artificial fish swarm algorithm for dynamic optimization problems , 2012, 2012 IEEE Congress on Evolutionary Computation.

[13]  Xiaodong Li,et al.  Using regression to improve local convergence , 2007, 2007 IEEE Congress on Evolutionary Computation.

[14]  Mohammad Reza Meybodi,et al.  Optimization in Dynamic Environments Utilizing a Novel Method Based on Particle Swarm Optimization , 2013 .

[15]  Shiu Yin Yuen,et al.  Parameter control system of evolutionary algorithm that is aided by the entire search history , 2012, Appl. Soft Comput..

[16]  Mohammad Reza Meybodi,et al.  Adaptive Particle Swarm Optimization Algorithm in Dynamic Environments , 2011, 2011 Third International Conference on Computational Intelligence, Modelling & Simulation.

[17]  Shengxiang Yang,et al.  Ant Colony Optimization Algorithms with Immigrants Schemes for the Dynamic Travelling Salesman Problem , 2013 .

[18]  Janez Brest,et al.  Modified firefly algorithm using quaternion representation , 2013, Expert Syst. Appl..

[19]  Surafel Luleseged Tilahun,et al.  Modified Firefly Algorithm , 2012, J. Appl. Math..

[20]  Shengxiang Yang,et al.  Particle Swarm Optimization With Composite Particles in Dynamic Environments , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[21]  Jürgen Branke,et al.  Memory enhanced evolutionary algorithms for changing optimization problems , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[22]  Mohammad Reza Meybodi,et al.  novel multi-swarm algorithm for optimization in dynamic environments based n particle swarm optimization , 2013 .

[23]  Jürgen Branke,et al.  Multiswarms, exclusion, and anti-convergence in dynamic environments , 2006, IEEE Transactions on Evolutionary Computation.

[24]  Xin-She Yang,et al.  Cuckoo Search and Firefly Algorithm: Theory and Applications , 2013 .

[25]  Xin-She Yang,et al.  Nature-Inspired Metaheuristic Algorithms , 2008 .

[26]  Shiu Yin Yuen,et al.  Parameter control by the entire search history: Case study of history-driven evolutionary algorithm , 2010, IEEE Congress on Evolutionary Computation.

[27]  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).

[28]  Xiaodong Li,et al.  Particle Swarms for Dynamic Optimization Problems , 2008, Swarm Intelligence.

[29]  Shengxiang Yang,et al.  Dynamic Vehicle Routing: A Memetic Ant Colony Optimization Approach , 2013, Automated Scheduling and Planning.

[30]  Changhe Li,et al.  A General Framework of Multipopulation Methods With Clustering in Undetectable Dynamic Environments , 2012, IEEE Transactions on Evolutionary Computation.

[31]  Shengxiang Yang,et al.  Non-stationary problem optimization using the primal-dual genetic algorithm , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[32]  Mohammad Reza Meybodi,et al.  mNAFSA: A novel approach for optimization in dynamic environments with global changes , 2014, Swarm Evol. Comput..

[33]  Karsten Weicker,et al.  Performance Measures for Dynamic Environments , 2002, PPSN.

[34]  Jürgen Branke,et al.  Evolutionary optimization in uncertain environments-a survey , 2005, IEEE Transactions on Evolutionary Computation.

[35]  Shengxiang Yang,et al.  Memory Based on Abstraction for Dynamic Fitness Functions , 2008, EvoWorkshops.

[36]  William Rand,et al.  The effect of crossover on the behavior of the GA in dynamic environments: a case study using the shaky ladder hyperplane-defined functions , 2006, GECCO '06.

[37]  Xin Yao,et al.  Population-Based Incremental Learning With Associative Memory for Dynamic Environments , 2008, IEEE Transactions on Evolutionary Computation.

[38]  Shiu Yin Yuen,et al.  An Evolutionary Algorithm That Makes Decision Based on the Entire Previous Search History , 2011, IEEE Transactions on Evolutionary Computation.

[39]  Mohammad Reza Meybodi,et al.  A New Particle Swarm Optimization Algorithm for Dynamic Environments , 2010, SEMCCO.

[40]  Xiaodong Li,et al.  Particle swarm with speciation and adaptation in a dynamic environment , 2006, GECCO.

[41]  Seth D. Guikema,et al.  Enhanced speciation in particle swarm optimization for multi-modal problems , 2011, Eur. J. Oper. Res..

[42]  Shengxiang Yang,et al.  Evolutionary dynamic optimization: A survey of the state of the art , 2012, Swarm Evol. Comput..

[43]  Ming-Huwi Horng,et al.  Multilevel minimum cross entropy threshold selection based on the firefly algorithm , 2011, Expert Syst. Appl..

[44]  V. Mani,et al.  Clustering using firefly algorithm: Performance study , 2011, Swarm Evol. Comput..

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

[46]  Mohammad Reza Meybodi,et al.  Speciation based firefly algorithm for optimization in dynamic environments , 2012 .

[47]  Trung Thanh Nguyen,et al.  Continuous dynamic optimisation using evolutionary algorithms , 2011 .

[48]  Mohammad Reza Meybodi,et al.  A Gaussian Firefly Algorithm , 2011 .

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