A Memetic PSO Algorithm for Scalar Optimization Problems

In this paper we introduce line search strategies originating from continuous optimization for the realization of the guidance mechanism in particle swarm optimization for scalar optimization problems. Since these techniques are well-suited for-but not restricted to-local search the resulting algorithm can be considered to be memetic. Further, we will use the same techniques for the construction of a new variant of a hill climber. We will discuss possible realizations and will finally present some numerical results indicating the strength of the two algorithms

[1]  L. Armijo Minimization of functions having Lipschitz continuous first partial derivatives. , 1966 .

[2]  John E. Dennis,et al.  Numerical methods for unconstrained optimization and nonlinear equations , 1983, Prentice Hall series in computational mathematics.

[3]  Pablo Moscato,et al.  On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts : Towards Memetic Algorithms , 1989 .

[4]  E. Allgower,et al.  Numerical Continuation Methods , 1990 .

[5]  Eugene L. Allgower,et al.  Numerical continuation methods - an introduction , 1990, Springer series in computational mathematics.

[6]  John H. Holland,et al.  When will a Genetic Algorithm Outperform Hill Climbing , 1993, NIPS.

[7]  Hans-Georg Beyer,et al.  Toward a Theory of Evolution Strategies: The (, )-Theory , 1994, Evolutionary Computation.

[8]  Terry Jones,et al.  Crossover, Macromutationand, and Population-Based Search , 1995, ICGA.

[9]  F. Oppacher,et al.  Hybridized crossover-based search techniques for program discovery , 1995, Proceedings of 1995 IEEE International Conference on Evolutionary Computation.

[10]  Hiroaki Satoh,et al.  Minimal generation gap model for GAs considering both exploration and exploitation , 1996 .

[11]  David B. Fogel,et al.  A Preliminary Investigation into Directed Mutations in Evolutionary Algorithms , 1996, PPSN.

[12]  David B. Fogel,et al.  Evolutionary Computation: The Fossil Record , 1998 .

[13]  Xin Yao,et al.  Evolutionary programming made faster , 1999, IEEE Trans. Evol. Comput..

[14]  William E. Hart,et al.  Evolutionary pattern search algorithms for unconstrained and linearly constrained optimization , 2001, IEEE Trans. Evol. Comput..

[15]  Kalyanmoy Deb,et al.  A Computationally Efficient Evolutionary Algorithm for Real-Parameter Optimization , 2002, Evolutionary Computation.

[16]  Shigeyoshi Tsutsui,et al.  Advances in evolutionary computing: theory and applications , 2003 .

[17]  Carlos A. Coello Coello,et al.  Handling multiple objectives with particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[18]  Francisco Herrera,et al.  Real-Coded Memetic Algorithms with Crossover Hill-Climbing , 2004, Evolutionary Computation.

[19]  Oliver Schütze,et al.  On Continuation Methods for the Numerical Treatment of Multi-Objective Optimization Problems , 2005, Practical Approaches to Multi-Objective Optimization.

[20]  Toward a Theory of Evolution Strategies : The ( p , A )-Theory , 2007 .

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