Improving the performance of particle swarms through dimension reductions — A case study with locust swarms

A key challenge for many heuristic search techniques is scalability — techniques that work well on low-dimension problems may perform poorly on high-dimension problems. To the extent that some problems/problem domains are separable, this can lead to a benefit for search techniques that can exploit separability. The standard algorithm for particle swarm optimization does not provide opportunities to exploit separable problems. However, the design of locust swarms involves two phases (scouts and swarms), and “dimension reductions” can be easily implemented during the scouts phase. This ability to exploit separability in locust swarms leads to large performance improvements on separable problems. More interestingly, dimension reductions can also lead to significant performance improvements on non-separable problems. Results on the Black-Box Optimization Benchmarking (BBOB) problems show how dimension reductions can help locust swarms perform better than standard particle swarms — especially on high-dimension problems.

[1]  Jing J. Liang,et al.  Dynamic multi-swarm particle swarm optimizer with local search for Large Scale Global Optimization , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[2]  Ponnuthurai Nagaratnam Suganthan,et al.  Benchmark Functions for the CEC'2013 Special Session and Competition on Large-Scale Global Optimization , 2008 .

[3]  James Montgomery,et al.  An analysis of the operation of differential evolution at high and low crossover rates , 2010, IEEE Congress on Evolutionary Computation.

[4]  Stephen Chen,et al.  Locust Swarms - A new multi-optima search technique , 2009, 2009 IEEE Congress on Evolutionary Computation.

[5]  Anne Auger,et al.  Real-Parameter Black-Box Optimization Benchmarking 2009: Noiseless Functions Definitions , 2009 .

[6]  James Kennedy,et al.  Defining a Standard for Particle Swarm Optimization , 2007, 2007 IEEE Swarm Intelligence Symposium.

[7]  Janez Brest,et al.  High-dimensional real-parameter optimization using Self-Adaptive Differential Evolution algorithm with population size reduction , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[8]  Ken Miura,et al.  Analyzing the Role of "Smart" Start Points in Coarse Search-Greedy Search , 2007, ACAL.

[9]  R. Eberhart,et al.  Particle Swarm Optimization-Neural Networks, 1995. Proceedings., IEEE International Conference on , 2004 .

[10]  Stephen Chen,et al.  An Analysis of Locust Swarms on Large Scale Global Optimization Problems , 2009, ACAL.

[11]  James Montgomery.  Differential evolution: Difference vectors and movement in solution space , 2009, 2009 IEEE Congress on Evolutionary Computation.

[12]  Fred W. Glover,et al.  Tabu Search - Part I , 1989, INFORMS J. Comput..

[13]  Mohammed El-Abd,et al.  Black-box optimization benchmarking for noiseless function testbed using particle swarm optimization , 2009, GECCO '09.

[14]  P. Moscato.  A Competitive-cooperative Approach to Complex Combinatorial Search , 1991 .

[15]  Tim Hendtlass,et al.  Particle Swarm Optimisation and high dimensional problem spaces , 2009, 2009 IEEE Congress on Evolutionary Computation.

[16]  Shang-Jeng Tsai,et al.  Solving large scale global optimization using improved Particle Swarm Optimizer , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[17]  Chun Chen,et al.  Multiple trajectory search for Large Scale Global Optimization , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[18]  Hans-Paul Schwefel,et al.  Evolution strategies – A comprehensive introduction , 2002, Natural Computing.

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

[20]  Tim Hendtlass,et al.  WoSP: a multi-optima particle swarm algorithm , 2005, 2005 IEEE Congress on Evolutionary Computation.

[21]  Xin Yao,et al.  Multilevel cooperative coevolution for large scale optimization , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[22]  Fred Glover,et al.  Tabu Search - Part II , 1989, INFORMS J. Comput..