A novel multi-population cultural algorithm adopting knowledge migration

Abstract

In existing multi-population cultural algorithms, information is exchanged among sub-populations by individuals. However, migrated individuals cannot reflect enough evolutionary information, which limits the evolution performance. In order to enhance the migration efficiency, a novel multi-population cultural algorithm adopting knowledge migration is proposed. Implicit knowledge extracted from the evolution process of each sub-population directly reflects the information about dominant search space. By migrating knowledge among sub-populations at the constant intervals, the algorithm realizes more effective interaction with less communication cost. Taken benchmark functions with high-dimension as the examples, simulation results indicate that the algorithm can effectively improve the speed of convergence and overcome premature convergence.

Leveraged Neighborhood Restructuring in Cultural Algorithms for Solving Real-World Numerical Optimization Problems

“…If both conditions (13) and (14) are satisfied, then the bounding boxes of the corresponding knowledge sources are said to overlap. The search for differences between the agents in the population is an important factor in the current search mode.…”

“…CAs have shown their ability when used to tackle problems from different application domains [11]- [14], [16], [17], [18]- [20].…”

“…These small-sized formations of tribes in the population perform the search in the problem landscape using their own fitness information. However, some of the tribe formations may get trapped by converging to a local optimum due to the accelerated convergence behavior of CA [11], [14]- [15]. So, selected experiences are sporadically injected into the overall population of tribes in order to enhance its diversity in all layers of the network.…”

An Improved Population Migration Algorithm Introducing the Local Search Mechanism of the Leap-Frog Algorithm and Crossover Operator

“…Ouyang et al introduced the simplex in the PMA to improve the search performance of the algorithm [5]. Guo et al increased performance of the population migration by improving the migrations’ efficiency [6]. Karaboga improved the bee swarm [7].…”

Validating a Peer-to-Peer Evolutionary Algorithm

“…In this context and under the term of P2P optimization, many optimization heuristics such as Evolutionary Algorithms (EAs), Particle Swarm (PSO) or Branch-and-bound have been re-designed in order to take advantage of such computing platforms [16,13,5,3]. The key issue here is that gathering a large amount of computational devices pose a whole set of practical problems.…”