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Author

Millie Pant

Other affiliations: Indian Institutes of Technology
Bio: Millie Pant is an academic researcher from Indian Institute of Technology Roorkee. The author has contributed to research in topics: Differential evolution & Particle swarm optimization. The author has an hindex of 35, co-authored 309 publications receiving 4602 citations. Previous affiliations of Millie Pant include Indian Institutes of Technology.


Papers
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Journal ArticleDOI
TL;DR: The journey of Differential Evolution is shown through its basic aspects like population generation, mutation schemes, crossover schemes, variation in parameters and hybridized variants along with various successful applications of DE.

316 citations

Journal ArticleDOI
TL;DR: An attempt is made to review the hybrid optimization techniques in which one main algorithm is a well known metaheuristic; particle swarm optimization or PSO and three hybrid PSO algorithms are compared on a test suite of nine conventional benchmark problems.

246 citations

Journal ArticleDOI
TL;DR: The proposed algorithm, named Multi-Objective Differential Evolution Algorithm (MODEA) utilizes the advantages of Opposition-Based Learning for generating an initial population of potential candidates and the concept of random localization in mutation step to introduce a new selection mechanism for generating a well distributed Pareto optimal front.

221 citations

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TL;DR: This paper provides a systematic literature survey of techniques for brain tumor segmentation and classification of abnormality and normality from MRI images based on different methods including deep learning techniques, metaheuristic techniques and hybridization of these two.

170 citations

Journal ArticleDOI
01 Jan 2014-Optik
TL;DR: In this paper, the authors applied differential evolution (DE) algorithm to balance the tradeoff between robustness and imperceptibility by exploring multiple scaling factors in image watermarking.

168 citations


Cited by
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01 Jan 2002

9,314 citations

Journal ArticleDOI
TL;DR: The results of the test functions prove that the proposed ALO algorithm is able to provide very competitive results in terms of improved exploration, local optima avoidance, exploitation, and convergence, showing that this algorithm has merits in solving constrained problems with diverse search spaces.

2,265 citations

Journal ArticleDOI
TL;DR: This work presents a comprehensive survey of the advances with ABC and its applications and it is hoped that this survey would be very beneficial for the researchers studying on SI, particularly ABC algorithm.
Abstract: Swarm intelligence (SI) is briefly defined as the collective behaviour of decentralized and self-organized swarms. The well known examples for these swarms are bird flocks, fish schools and the colony of social insects such as termites, ants and bees. In 1990s, especially two approaches based on ant colony and on fish schooling/bird flocking introduced have highly attracted the interest of researchers. Although the self-organization features are required by SI are strongly and clearly seen in honey bee colonies, unfortunately the researchers have recently started to be interested in the behaviour of these swarm systems to describe new intelligent approaches, especially from the beginning of 2000s. During a decade, several algorithms have been developed depending on different intelligent behaviours of honey bee swarms. Among those, artificial bee colony (ABC) is the one which has been most widely studied on and applied to solve the real world problems, so far. Day by day the number of researchers being interested in ABC algorithm increases rapidly. This work presents a comprehensive survey of the advances with ABC and its applications. It is hoped that this survey would be very beneficial for the researchers studying on SI, particularly ABC algorithm.

1,645 citations

Journal ArticleDOI
TL;DR: The components and concepts that are used in various metaheuristics are outlined in order to analyze their similarities and differences and the classification adopted in this paper differentiates between single solution based metaheURistics and population based meta heuristics.

1,343 citations

Journal ArticleDOI
TL;DR: It is found 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 on DE.
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.

1,265 citations