Gourab Ghosh Roy

An Improved Harmony Search Algorithm with Differential Mutation Operator

Harmony Search (HS) is a recently developed stochastic algorithm which imitates the music improvisation process. In this process, the musicians improvise their instrument pitches searching for the perfect state of harmony. Practical experiences, however, suggest that the algorithm suffers from the problems of slow and/or premature convergence over multimodal and rough fitness landscapes. This paper presents an attempt to improve the search performance of HS by hybridizing it with Differential Evolution (DE) algorithm. The performance of the resulting hybrid algorithm has been compared with classical HS, the global best HS, and a very popular variant of DE over a test-suite of six well known benchmark functions and one interesting practical optimization problem. The comparison is based on the following performance indices - (i) accuracy of final result, (ii) computational speed, and (iii) frequency of hitting the optima.

Design of Non-Uniform Circular Antenna Arrays Using a Modified Invasive Weed Optimization Algorithm

An ecologically inspired optimization algorithm, called invasive weed optimization (IWO), is presented for the design of non-uniform, planar, and circular antenna arrays that can achieve minimum side lobe levels for a specific first null beamwidth while avoiding the mutual coupling effects simultaneously. IWO recently emerged as a derivative-free real parameter optimizer that mimics the ecological behavior of colonizing weeds. For the present application, classical IWO has been modified by introducing a more explorative routine of changing the standard deviation of the seed population (equivalent to mutation step-size in evolutionary algorithms) of the algorithm. Simulation results over three significant instances of the circular array design problem have been presented to illustrate the effectiveness of the modified IWO algorithm. The design results obtained with modified IWO have been shown to comfortably beat those obtained with other state-of-the-art metaheuristics like genetic algorithm (GA), particle swarm optimization (PSO), original IWO and differential evolution (DE) in a statistically meaningful way.

On convergence of the multi-objective particle swarm optimizers

Several variants of the particle swarm optimization (PSO) algorithm have been proposed in recent past to tackle the multi-objective optimization (MO) problems based on the concept of Pareto optimality. Although a plethora of significant research articles have so far been published on analysis of the stability and convergence properties of PSO as a single-objective optimizer, till date, to the best of our knowledge, no such analysis exists for the multi-objective PSO (MOPSO) algorithms. This paper presents a first, simple analysis of the general Pareto-based MOPSO and finds conditions on its most important control parameters (the inertia factor and acceleration coefficients) that govern the convergence behavior of the algorithm to the optimal Pareto front in the objective function space. Computer simulations over benchmark MO problems have also been provided to substantiate the theoretical derivations.

Discrete harmony search based expert model for epileptic seizure detection in electroencephalography

Seizure detection and classification using signal processing methods has been an important issue of research for the last two decades. In the present study, a novel scheme was presented to detect epileptic seizure activity with very fast and highest accuracy from background electro encephalogram (EEG) data recorded from epileptic and normal subjects. The proposed scheme is based on discrete wavelet packet transform (DWT) with energy, entropy, standard deviation, mean, kurtosis, skewness and entropy estimation at each node of the decomposition tree followed by application of probabilistic neural network (PNN). Normal as well as epileptic EEG epochs were decomposed into approximation and details coefficients till sixth-level using DWT packet. Discrete harmony search with modified differential operator was used to select the optimal features out of all above mentioned statistical and non-statistical parameters. In order to demonstrate the efficacy of the proposed algorithm for classification purpose using PNN, we have implemented 10-fold cross validation. Clinical EEG data recorded from normal as well as epileptic subjects are used to test the performance of this new scheme. It is found that the detection rate is 100% accurate with same level of sensitivity and specificity.

Artificial foraging weeds for global numerical optimization over continuous spaces

Invasive Weed Optimization (IWO) is a recently developed derivative-free metaheuristic algorithm that mimics the robust process of weeds colonization and distribution in an ecosystem. On the other hand central to an ecosystem is the foraging behavior that pertains to the act of searching for food and forms an integral part of the daily life of most of the living creatures. For over past two decades, a few significant optimization algorithms were developed by emulating the foraging behavior of creatures like ants, bacteria, fish, bees etc. This article presents a hybrid real-parameter optimizer developed by incorporating the principles of Optimal Foraging Theory (OFT) in IWO, with a view to improving the search mechanism of the latter over discontinuous and multi-modal fitness landscapes, riddled with local optima. The hybridization does not impose any serious computational burden on IWO in terms of increasing number of Function Evaluations (FEs). The performance of the resulting hybrid algorithm has been compared with eleven other state-of-the-art metaheuristic algorithms over a test-suite of 16 numerical benchmarks taken from the CEC (Congress on Evolutionary Computation) 2005 competition and special session on real parameter optimization. Our simulation experiments indicate that the proposed algorithm is able to attain comparable results against the nine other optimizers. Owing to its promising performance on benchmarks and ease of implementation (without requiring much programming overhead), the proposed algorithm may serve as an attractive alternative for a plethora of practical optimization problems.

Designing fractional-order PI λ D μ controller using differential harmony search algorithm

Harmony Search (HS) has recently emerged as an efficient metaheuristic algorithm that draws inspiration from the music improvisation process. This article describes the design of fractional-order proportional-integral-derivative (FOPID) controllers, using a newly developed variant of HS, known as differential harmony search (DHS). Design of FOPID controllers is more complex than that of conventional integer-order PID controller since the latter involves only three parameters while the former involves five parameters to tune. Controller synthesis is based on user specifications like peak overshoot and, rise time; which are used to formulate a single objective optimisation problem. Tustin operator-based continuous fraction expansion (CFE) scheme was used to digitally realise fractional-order closed loop transfer function of the designed plant-controller setup. Experimental results of comparison between DHS and a few established optimisation techniques [particle swarm optimisation (PSO) and genetic algorithm (GA)] over different instantiations of the design problem reflect the superiority of the proposed methodology.

On optimal feature selection using modified Harmony search for power quality disturbance classification

This paper presents a novel technique for power quality disturbance classification. Wavelet Transform (WT) has been used to extract some useful features of the power system disturbance signal and Discrete Harmony Search with Modified Differential mutation operator (DHS_MD) have been used for feature dimension reduction in order to achieve high classification accuracy. Next, a Probabilistic Neural Network (PNN) has been trained using the optimal feature set selected by DHS_MD for automatic PQ disturbance classification. Considering ten types of PQ disturbances, simulations have been carried out which show that the combination of feature extraction by WT followed by feature reduction using DHS_MD increases the testing accuracy of PNN while classifying PQ signals.

On convergence of multi-objective Particle Swarm Optimizers

Several variants of the Particle Swarm Optimization (PSO) algorithm have been proposed in recent past to tackle the multi-objective optimization problems based on the concept of Pareto optimality. Although a plethora of significant research articles have so far been published on analysis of the stability and convergence properties of PSO as a single-objective optimizer, till date, to the best of our knowledge, no such analysis exists for the multi-objective PSO (MOPSO) algorithms. This paper presents a first, simple analysis of the general Pareto-based MOPSO and finds conditions on its most important control parameters (the inertia factor and acceleration coefficients) that control the convergence behavior of the algorithm to the Pareto front in the objective function space. Limited simulation supports have also been provided to substantiate the theoretical derivations.

On population variance and explorative power of invasive weed optimization algorithm

Theoretical analysis of mataheuristic algorithms is believed to be very important for understanding their internal search mechanism and thus to develop more efficient algorithms. In this article we present a simple mathematical analysis of the explorative search behavior of a recently developed metaheuristic algorithm called Invasive Weed Optimization (IWO). IWO is a novel ecologically inspired algorithm that mimics the process of weeds colonization and distribution. This work analyses the evolution of the population-variance over successive generations in IWO and thereby draws some important conclusions regarding the explorative power of the same. Experimental results have been provided to validate the theoretical treatment.

Erratum to On convergence of the multi-objective particle swarm optimizers [Inform. Sci. 181 (2011) 1411-1425]

Erratum to ‘‘On convergence of the multi-objective particle swarm optimizers’’ [Inform. Sci. 181 (2011) 1411–1425] Prithwish Chakraborty , Swagatam Das a,⇑, Gourab Ghosh Roy , Ajith Abraham b a Dept. of Electronics and Telecommunication Eng., Jadavpur University, Kolkata, India b Machine Intelligence Research (MIR Labs), Scientific Network for Innovation and Research Excellence, P.O. Box 2259, Auburn, WA 98071-2259, USA