An extended mutation concept for the local selection based differential evolution algorithm

2011 IEEE Symposium on Differential Evolution (SDE)

Plagianakos²,

Vrahatis

2011

Abstract-Handling multimodal functions is a very important and challenging task in evolutionary computation community, since most of the real-world applications exhibit highly multimodal landscapes. Motivated by the dynamics and the proximity characteristics of Differential Evolution's mutation strategies tending to distribute the individuals of the population to the vicinity of the problem's minima, we introduce two new Differential Evolution mutation strategies. The new mutation strategies incorporate spatial information about the neighborhood of each potential solution and exhibit a niching formation, without incorporating any additional parameter. Experimental results on eight well known multimodal functions and comparisons with some state-of-the-art algorithms indicate that the proposed mutation strategies are competitive and very promising, since they are able to reliably locate and maintain many global optima throughout the evolution process.

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Finding multiple global optima exploiting differential evolution's niching capability

2011 IEEE Symposium on Differential Evolution (SDE)

Plagianakos²,

Vrahatis

2011

“…Moreover, DE with local selection [21] designs a mutation strategy with two main components: a local and a global mutation rule. Throughout the evolutionary process, the two rules are probabilistically selected by a fixed and pre-specified probability.…”

Section: Related Workmentioning

confidence: 99%

“…If the new individual is qualified for insertion then it is kept in the archive. If the solution is already in the archive (Algorithm 1, lines [20][21][22] then this solution is re-initialized within the bounds of the problem at hand, in an attempt to search for unexplored regions. The algorithmic scheme of the proposed algorithm (dADE/nrand/1) and the dynamic archive are briefly illustrated in Algorithm 1 and 2 respectively.…”

Section: Archivementioning

confidence: 99%

A dynamic archive niching differential evolution algorithm for multimodal optimization

2013 IEEE Congress on Evolutionary Computation

Burke

2013

Abstract-Highly multimodal landscapes with multiple local/global optima represent common characteristics in real-world applications. Many niching algorithms have been proposed in the literature which aim to search such landscapes in an attempt to locate as many global optima as possible. However, to locate and maintain a large number of global solutions, these algorithms are substantially influenced by their parameter values, such as a large population size. Here, we propose a new niching Differential Evolution algorithm that attempts to overcome the population size influence and produce good performance almost independently of its population size. To this end, we incorporate two mechanisms into the algorithm: a control parameter adaptation technique and an external dynamic archive along with a reinitialization mechanism. The first mechanism is designed to efficiently adapt the control parameters of the algorithm, whilst the second one is responsible for enabling the algorithm to investigate unexplored regions of the search space and simultaneously keep the best solutions found by the algorithm. The proposed approach is compared with two Differential Evolution variants on a recently proposed benchmark suite. Empirical results indicate that the proposed niching algorithm is competitive and very promising. It exhibits a robust and stable behavior, whilst the incorporation of the dynamic archive seems to tackle the population size influence effectively. Moreover, it alleviates the problem of having to finetune the population size parameter in a niching algorithm.

“…Additionally, DE using local selection (DELS) [21] employs a new mutation strategy that divides the mutation operation into the local and the global mutation stages. It selects a different mutation strategy, with a pre-specified probability, to perform either a global or a local mutation.…”

Section: Introductionmentioning

confidence: 99%

Multimodal optimization using niching differential evolution with index-based neighborhoods

2012 IEEE Congress on Evolutionary Computation

Plagianakos

Vrahatis

2012

Abstract-A new family of Differential Evolution mutation strategies (DE/nrand) that are able to handle multimodal functions, have been recently proposed. The DE/nrand family incorporates information regarding the real nearest neighborhood of each potential solution, which aids them to accurately locate and maintain many global optimizers simultaneously, without the need of additional parameters. However, these strategies have increased computational cost. To alleviate this problem, instead of computing the real nearest neighbor, we incorporate an indexbased neighborhood into the mutation strategies. The new mutation strategies are evaluated on eight well-known and widely used multimodal problems and their performance is compared against five state-of-the-art algorithms. Simulation results suggest that the proposed strategies are promising and exhibit competitive behavior, since with a substantial lower computational cost they are able to locate and maintain many global optima throughout the evolution process.