MEMPSODE: A global optimization software based on hybridization of population-based algorithms and local searches

Description

MEMPSODE is a global optimization software tool that integrates two prominent population- based stochastic algorithms, namely Particle Swarm Optimization and Differential Evolution, with well established efficient local search procedures made available via the Merlin optimization environment. The resulting hybrid algorithms, also referred to as Memetic Algorithms, combine the space exploration advantage of their global part with the efficiency asset of the local search, and as expected they have displayed a highly efficient behavior in solving diverse optimization problems. The proposed software is carefully parametrized so as to offer complete control to fully exploit the algorithmic virtues. It is accompanied by comprehensive examples and a large set of widely used test functions, including tough atomic cluster and protein conformation problems.

MEMPSODE is described in the following article:

• MEMPSODE: A global optimization software based on hybridization of population-based algorithms and local searches
  C. Voglis, K.E. Parsopoulos, D.G. Papageorgiou, I.E. Lagaris, M.N. Vrahatis
  Comput. Phys. Commun. 183 (2012) 1139-1154.
  Associated software in the CPC Program Library: AELM_v1_0
  DOI: 10.1016/j.cpc.2012.01.010

Comparative results on the performance of the method can be found in:

• MEMPSODE: Comparing particle swarm optimization and differential evolution within a hybrid memetic global optimization framework
  C. Voglis, G.S. Piperagkas, K.E. Parsopoulos, D.G. Papageorgiou, I.E. Lagaris
  GECCO 2012, Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference companion, pp. 253-260
  DOI: 10.1145/2330784.2330821
• MEMPSODE: An empirical assessment of local search algorithm impact on a memetic algorithm using noiseless testbed
  C. Voglis, G.S. Piperagkas, K.E. Parsopoulos, D.G. Papageorgiou, I.E. Lagaris
  GECCO 2012, Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference companion, pp. 245-252
  DOI: 10.1145/2330784.2330820

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