C. Shunmuga Velayutham

A comparative performance analysis of Differential Evolution and Dynamic Differential Evolution variants

In this paper we present an empirical, comparative performance, analysis of fourteen variants of Differential Evolution (DE) and Dynamic Differential Evolution (DDE) algorithms to solve unconstrained global optimization problems. The aim is to compare DDE, which employs a dynamic evolution mechanism, against DE and to identify the competitive variants which perform reasonably well on problems with different features. The fourteen variants of DE and DDE are benchmarked on 6 test functions grouped by features - unimodal separable, unimodal nonseparable, multimodal separable and multimodal non-separable. The analysis identifies the competitive variants and shows that DDE variants consistently outperform their classical counter parts.

Empirical study on migration topologies and migration policies for island based distributed differential evolution variants

In this paper we present an empirical performance analysis of fourteen variants of Differential Evolution (DE) on a set of unconstrained global optimization problems. The island based distributed differential evolution counterparts of the above said 14 variants have been implemented with mesh and ring migration topologies and their superior performance over the serial implementation has been demonstrated. The competitive performance of ring topology based distributed differential evolution variants on the chosen problem has also been demonstrated. Six different migration policies are experimented for ring topology, and their performances are reported.

An empirical comparison of Differential Evolution variants on different classes of unconstrained global optimization problems

This paper presents an empirical analysis of the performance of Differential Evolution (DE) variants on different classes of unconstrained global optimization benchmark problems. This analysis has been undertaken to identify competitive DE variants which perform reasonably well on a range of problems with different features. Towards this, fourteen DE variants were implemented and tested on 14 high dimensional benchmark functions grouped by their modality and decomposability viz., unimodal separable, unimodal nonseparable, multimodal separable and multimodal nonseparable. This extensive performance analysis provides some insight about the competitiveness of DE variants in solving test problems with representative landscape features such as modality and decomposability.

Differential Evolution Based On-Line Feature Analysis in an Asymmetric Subsethood Product Fuzzy Neural Network

This paper proposes a novel differential evolution learning based online feature selection method in an asymmetric subsethood product fuzzy neural network (ASuPFuNIS). The fuzzy neural network has fuzzy weights modeled by asymmetric Gaussian fuzzy sets, mutual subsethood based activation spread, product aggregation operator thatworks in conjunction with volume defuzzification in a differential evolution learning framework. By virtue of a mixed floating point-binary genetic coding and a customized dissimilarity based bit flipping operator, the differential evolution based asymmetric subsethood product network is shown to have online feature selection capabilities on a synthetic data set.

Distributed mixed variant differential evolution algorithms for unconstrained global optimization

This paper proposes a novel distributed differential evolution algorithm called Distributed Mixed Variant Differential Evolution (dmvDE). To alleviate the time consuming trial-and-error selection of appropriate Differential Evolution (DE) variant to solve a given optimization problem, dmvDE proposes to mix effective DE variants with diverse characteristics in a distributed framework. The novelty of dmvDEs lies in mixing different DE variants in an island based distributed framework. The 19 dmvDE algorithms, discussed in this paper, constitute various proportions and combinations of four DE variants (DE/rand/1/bin, DE/rand/2/bin, DE/best/2/bin and DE/rand-to-best/1/bin) as subpopulations with each variant evolving independently but also exchanging information amongst others to co-operatively enhance the efficacy of the distributed DE as a whole. The dmvDE algorithms have been run on a set of test problems and compared to the distributed versions of the constituent DE variants. Simulation results show that dmvDEs display a consistent overall improvement in performance than that of distributed DEs. The best of dmvDE algorithms has also been benchmarked against five distributed differential evolution algorithms. Simulation results reiterate the superior performance of the mixing of the DE variants in a distributed frame work. The best of dmvDE algorithms outperforms, on average, all five algorithms considered.

Some applications of an asymmetric subsethood product fuzzy neural inference system

This paper presents some applications of an asymmetric subsethood product fuzzy neural inference system (ASuP-FuNIS). The ASuPFuNIS model extends SuPFuNIS by permitting signal and weight fuzzy sets to be modeled by asymmetric Gaussian membership functions. The asymmetric subsethood product network admits both numeric as well as linguistic inputs. Numeric inputs are fuzzified prior to their application to the network; linguistic inputs are presented without modification. The network architecture directly embeds fuzzy if-then rules, and connections represent antecedent and consequent fuzzy sets. The model uses mutual subsethood based activation spread and a product aggregation operator that works in conjunction with volume defuzzification in a gradient descent learning framework. The model is economical in terms of the number of rules required to solve difficult problems and is robust against random variations in data sets. Simulation results on three benchmark problems-the Hepatitis diagnosis, Iris data classification and the Narazaki-Ralescu function approximation problem-show that the subsethood based model performs excellently with minimal number of rules.

Theoretical Analysis of Expected Population Variance Evolution for a Differential Evolution Variant

In this paper we derive an analytical expression to describe the evolution of expected population variance for Differential Evolution (DE) variant—DE/current-to-best/1/bin (as a measure of its explorative power). The derived theoretical evolution of population variance has been validated by comparing it against the empirical evolution of population variance by DE/current-to-best/1/bin on four benchmark functions.

Distributed heterogeneous mixing of differential and dynamic differential evolution variants for unconstrained global optimization

This paper proposes a novel distributed differential evolution framework called distributed mixed variants (dynamic) differential evolution (

Is Differential Evolution Sensitive to Pseudo Random Number Generator Quality? – An Investigation

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