Durbadal Mandal

Bio: Durbadal Mandal is an academic researcher from National Institute of Technology, Durgapur. The author has contributed to research in topics: Particle swarm optimization & Antenna array. The author has an hindex of 27, co-authored 409 publications receiving 3297 citations. Previous affiliations of Durbadal Mandal include Hindustan College of Science and Technology.

Cat Swarm Optimization algorithm for optimal linear phase FIR filter design.

Abstract: In this paper a new meta-heuristic search method, called Cat Swarm Optimization (CSO) algorithm is applied to determine the best optimal impulse response coefficients of FIR low pass, high pass, band pass and band stop filters, trying to meet the respective ideal frequency response characteristics CSO is generated by observing the behaviour of cats and composed of two sub-models In CSO, one can decide how many cats are used in the iteration Every cat has its' own position composed of M dimensions, velocities for each dimension, a fitness value which represents the accommodation of the cat to the fitness function, and a flag to identify whether the cat is in seeking mode or tracing mode The final solution would be the best position of one of the cats CSO keeps the best solution until it reaches the end of the iteration The results of the proposed CSO based approach have been compared to those of other well-known optimization methods such as Real Coded Genetic Algorithm (RGA), standard Particle Swarm Optimization (PSO) and Differential Evolution (DE) The CSO based results confirm the superiority of the proposed CSO for solving FIR filter design problems The performances of the CSO based designed FIR filters have proven to be superior as compared to those obtained by RGA, conventional PSO and DE The simulation results also demonstrate that the CSO is the best optimizer among other relevant techniques, not only in the convergence speed but also in the optimal performances of the designed filters

Original Article: Design of optimal linear phase FIR high pass filter using craziness based particle swarm optimization technique

Abstract: In this paper, an optimal design of linear phase digital high pass FIR filter using Craziness based Particle Swarm Optimization (CRPSO) approach has been presented. FIR filter design is a multi-modal optimization problem. The conventional gradient based optimization techniques are not efficient for such multi-modal optimization problem as they are susceptible to getting trapped on local optima. Given the desired filter specifications to be realized, the CRPSO algorithm generates a set of optimal filter coefficients and tries to meet the desired specifications. In birds' flocking or fish schooling, a bird or a fish often changes directions suddenly. This is described by using a ''craziness'' factor and is modeled in the CRPSO technique. In this paper, the realizations of the CRPSO based optimal FIR high pass filters of different orders have been performed. The simulation results have been compared to those obtained by the well accepted classical optimization algorithm such as Parks and McClellan algorithm (PM), and evolutionary algorithms like Real Coded Genetic Algorithm (RGA), and conventional Particle Swarm Optimization (PSO). The results justify that the proposed optimal filter design approach using CRPSO outperforms PM, RGA and PSO, in the optimal characteristics of frequency spectrums.

Design of Concentric Circular Antenna Array with Central Element Feeding Using Particle Swarm Optimization with Constriction Factor and Inertia Weight Approach and Evolutionary Programing Technique

Abstract: In this paper the maximum sidelobe level (SLL) reductions without and with central element feeding in various designs of three-ring concentric circular antenna arrays (CCAA) are examined using a real-coded Evolutionary Programming (EP) to finally determine the global optimal three-ring CCAA design. Standard real-coded Particle Swarm Optimization (PSO) and real-coded Particle Swarm Optimization with Constriction Factor and Inertia Weight Approach (PSOCFIWA) are also employed for comparative optimization but both prove to be suboptimal. This paper assumes non-uniform excitation weights and uniform spacing of excitation elements in each three-ring CCAA design. Among the various CCAA designs, the design containing central element and 4, 6 and 8 elements in three successive concentric rings proves to be such global optimal design set with global minimum SLL (−39.66 dB) as determined by Evolutionary Programming.

Design of Concentric Circular Antenna Array with Central Element Feeding Using Particle Swarm Optimization with Constriction Factor and Inertia Weight Approach and Evolutionary Programing Technique

Abstract: In this paper the maximum sidelobe level (SLL) reductions without and with central element feeding in various designs of three-ring concentric circular antenna arrays (CCAA) are examined using a real-coded Evolutionary Programming (EP) to finally determine the global optimal three-ring CCAA design. Standard real-coded Particle Swarm Optimization (PSO) and real-coded Particle Swarm Optimization with Constriction Factor and Inertia Weight Approach (PSOCFIWA) are also employed for comparative optimization but both prove to be suboptimal. This paper assumes non-uniform excitation weights and uniform spacing of excitation elements in each three-ring CCAA design. Among the various CCAA designs, the design containing central element and 4, 6 and 8 elements in three successive concentric rings proves to be such global optimal design set with global minimum SLL (−39.66 dB) as determined by Evolutionary Programming.

Craziness based Particle Swarm Optimization algorithm for FIR band stop filter design

Abstract: In this paper, an improved particle swarm optimization technique called Craziness based Particle Swarm Optimization (CRPSO) is proposed and employed for digital finite impulse response (FIR) band stop filter design The design of FIR filter is generally nonlinear and multimodal Hence gradient based classical optimization methods are not suitable for digital filter design due to sub-optimality problem So, global optimization techniques are required to avoid local minima problem Several heuristic approaches are available in the literatures The Particle Swarm Optimization (PSO) algorithm is a heuristic approach with two main advantages: it has fast convergence, and it uses only a few control parameters But the performance of PSO depends on its parameters and may be influenced by premature convergence and stagnation problem To overcome these problems the PSO algorithm has been modified in this paper and is used for FIR filter design In birds' flocking or fish schooling, a bird or a fish often changes directions suddenly This is described by using a “craziness” factor and is modelled in the technique by using a craziness variable A craziness operator is introduced in the proposed technique to ensure that the particle would have a predefined craziness probability to maintain the diversity of the particles The algorithm's performance is studied with the comparison of real coded genetic algorithm (RGA), conventional PSO, comprehensive learning particle swarm optimization (CLPSO) and Parks and McClellan (PM) Algorithm The simulation results show that the CRPSO is superior or comparable to the other algorithms for the employed examples and can be efficiently used for FIR filter design

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Recent advances in differential evolution – An updated survey

Abstract: Differential Evolution (DE) is arguably one of the most powerful and versatile evolutionary optimizers for the continuous parameter spaces in recent times. Almost 5 years have passed since the first comprehensive survey article was published on DE by Das and Suganthan in 2011. Several developments have been reported on various aspects of the algorithm in these 5 years and the research on and with DE have now reached an impressive state. Considering the huge progress of research with DE and its applications in diverse domains of science and technology, we find that it is a high time to provide a critical review of the latest literatures published and also to point out some important future avenues of research. The purpose of this paper is to summarize and organize the information on these current developments on DE. Beginning with a comprehensive foundation of the basic DE family of algorithms, we proceed through the recent proposals on parameter adaptation of DE, DE-based single-objective global optimizers, DE adopted for various optimization scenarios including constrained, large-scale, multi-objective, multi-modal and dynamic optimization, hybridization of DE with other optimizers, and also the multi-faceted literature on applications of DE. The paper also presents a dozen of interesting open problems and future research issues on DE.

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Abstract: Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms.

Monarch butterfly optimization

Abstract: In nature, the eastern North American monarch population is known for its southward migration during the late summer/autumn from the northern USA and southern Canada to Mexico, covering thousands of miles. By simplifying and idealizing the migration of monarch butterflies, a new kind of nature-inspired metaheuristic algorithm, called monarch butterfly optimization (MBO), a first of its kind, is proposed in this paper. In MBO, all the monarch butterfly individuals are located in two distinct lands, viz. southern Canada and the northern USA (Land 1) and Mexico (Land 2). Accordingly, the positions of the monarch butterflies are updated in two ways. Firstly, the offsprings are generated (position updating) by migration operator, which can be adjusted by the migration ratio. It is followed by tuning the positions for other butterflies by means of butterfly adjusting operator. In order to keep the population unchanged and minimize fitness evaluations, the sum of the newly generated butterflies in these two ways remains equal to the original population. In order to demonstrate the superior performance of the MBO algorithm, a comparative study with five other metaheuristic algorithms through thirty-eight benchmark problems is carried out. The results clearly exhibit the capability of the MBO method toward finding the enhanced function values on most of the benchmark problems with respect to the other five algorithms. Note that the source codes of the proposed MBO algorithm are publicly available at GitHub ( https://github.com/ggw0122/Monarch-Butterfly-Optimization , C++/MATLAB) and MATLAB Central ( http://www.mathworks.com/matlabcentral/fileexchange/50828-monarch-butterfly-optimization , MATLAB).