Optimal power allocation and joint source-channel coding for wireless DS-CDMA visual sensor networks using the Nash Bargaining Solution

We investigate the performance of the recently proposed unified particle swarm optimization algorithm on two categories of operations research problems, namely minimax and integer programming problems. Different variants of the algorithm are employed and compared with established variants of the particle swarm optimization algorithm. Statistical hypothesis testing is performed to justify the significance of the results. Conclusions regarding the ability of the unified particle swarm optimization method to tackle operations research problems as well as on the performance of each variant are derived and discussed.

[1]  R. Faure,et al.  Introduction to operations research , 1968 .

[2]  C. Charalambous,et al.  Nonlinear programming using minimax techniques , 1974 .

[3]  G. Hogg,et al.  UNCONSTRAINED DISCRETE NONLINEAR PROGRAMMING , 1979 .

[4]  Editors , 1986, Brain Research Bulletin.

[5]  Günter Rudolph,et al.  An Evolutionary Algorithm for Integer Programming , 1994, PPSN.

[6]  P. Pardalos,et al.  Minimax and applications , 1995 .

[7]  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.

[8]  Hans-Paul Schwefel,et al.  Evolution and optimum seeking , 1995, Sixth-generation computer technology series.

[9]  Song Xu,et al.  Smoothing Method for Minimax Problems , 2001, Comput. Optim. Appl..

[10]  Michael N. Vrahatis,et al.  Particle swarm optimization for integer programming , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[11]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[12]  Michael N. Vrahatis,et al.  Particle swarm optimization for minimax problems , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

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

[14]  Ioan Cristian Trelea,et al.  The particle swarm optimization algorithm: convergence analysis and parameter selection , 2003, Inf. Process. Lett..

[15]  José Neves,et al.  The fully informed particle swarm: simpler, maybe better , 2004, IEEE Transactions on Evolutionary Computation.

[16]  Michael N. Vrahatis,et al.  Unified Particle Swarm Optimization in Dynamic Environments , 2005, EvoWorkshops.

[17]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[18]  Tim Blackwell,et al.  Particle Swarm Optimization in Dynamic Environments , 2007, Evolutionary Computation in Dynamic and Uncertain Environments.

[19]  Mauro Birattari,et al.  Swarm Intelligence , 2012, Lecture Notes in Computer Science.

[20]  Konstantinos E. Parsopoulos,et al.  UPSO: A Unified Particle Swarm Optimization Scheme , 2019, International Conference of Computational Methods in Sciences and Engineering 2004 (ICCMSE 2004).