The Experts below are selected from a list of 51276 Experts worldwide ranked by ideXlab platform
Satvir Singh - One of the best experts on this subject based on the ideXlab platform.
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Improved artificial bee colony metaheuristic for energy-efficient clustering in wireless sensor networks
Artificial Intelligence Review, 2019Co-Authors: Palvinder Singh Mann, Satvir SinghAbstract:Energy-efficient clustering is a well known NP-hard optimization problem for complex and dynamic Wireless sensor networks (WSNs) environment. Swarm intelligence (SI) based metaheuristic like Ant colony optimization, Particle swarm optimization and more recently Artificial bee colony (ABC) has shown desirable properties of being adaptive to solve optimization problem of energy efficient clustering in WSNs. ABC arose much interest over other population-based Metaheuristics for solving optimization problems in WSNs due to ease of implementation however, its search equation contributes to its insufficiency due to poor exploitation phase and storage of certain control parameters. Thus, we propose an improved Artificial bee colony (iABC) metaheuristic with an improved search equation to enhance its exploitation capabilities and in order to increase the global convergence of the proposed metaheuristic, an improved population sampling technique is introduced through Student’s-t distribution, which require only one control parameter to compute and store, hence increase efficiency of proposed metaheuristic. The proposed metaheuristic maintain a good balance between exploration and exploitation search abilities with least memory requirements, moreover the use of first of its kind compact Student’s-t distribution, make it suitable for limited hardware requirements of WSNs. Further, an energy efficient bee clustering protocol based on iABC metaheuristic is introduced, which inherit the capabilities of the proposed metaheuristic to obtain optimal cluster heads and improve energy efficiency in WSNs. Simulation results show that the proposed clustering protocol outperforms other well known SI based protocols on the basis of packet delivery, throughput, energy consumption and extend network lifetime.
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Improved metaheuristic-based energy-efficient clustering protocol with optimal base station location in wireless sensor networks
Soft Computing, 2019Co-Authors: Palvinder Singh Mann, Satvir SinghAbstract:Efficient clustering is a well-documented NP-hard optimization problem in wireless sensor networks (WSNs). Variety of computational intelligence techniques including evolutionary algorithms, reinforcement learning, artificial immune systems and recently, artificial bee colony (ABC) metaheuristic have been applied for efficient clustering in WSNs. Due to ease of use and adaptive nature, ABC arose much interest over other population-based Metaheuristics for solving optimization problems in WSNs. However, its search equation contributes to its insufficiency due to comparably poor exploitation cycle and requirement of certain control parameters. Thus, we propose an improved artificial bee colony (iABC) metaheuristic with an improved solution search equation to improve exploitation capabilities of existing metaheuristic. Further, to enhance the global convergence of the proposed metaheuristic, an improved population sampling technique is introduced through Student’s t-distribution, which require only one control parameter to compute and store and therefore increase efficiency of proposed metaheuristic. The proposed metaheuristic maintain a good balance between exploration and exploitation search abilities with least memory requirements; moreover, the use of first-of-its-kind compact Student’s t-distribution makes it suitable for limited hardware requirements of WSNs. Additionally, an energy-efficient clustering protocol based on iABC metaheuristic is presented, which inherits the capabilities of the proposed metaheuristic to obtain optimal cluster heads along with an optimal base station location to improve energy efficiency in WSNs. Simulation results show that the proposed clustering protocol outperforms other well-known protocols on the basis of packet delivery, throughput, energy consumption, network lifetime and latency as performance metric.
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Butterfly optimization algorithm: a novel approach for global optimization
Soft Computing, 2019Co-Authors: Sankalap Arora, Satvir SinghAbstract:Real-world problems are complex as they are multidimensional and multimodal in nature that encourages computer scientists to develop better and efficient problem-solving methods. Nature-inspired Metaheuristics have shown better performances than that of traditional approaches. Till date, researchers have presented and experimented with various nature-inspired metaheuristic algorithms to handle various search problems. This paper introduces a new nature-inspired algorithm, namely butterfly optimization algorithm (BOA) that mimics food search and mating behavior of butterflies, to solve global optimization problems. The framework is mainly based on the foraging strategy of butterflies, which utilize their sense of smell to determine the location of nectar or mating partner. In this paper, the proposed algorithm is tested and validated on a set of 30 benchmark test functions and its performance is compared with other metaheuristic algorithms. BOA is also employed to solve three classical engineering problems (spring design, welded beam design, and gear train design). Results indicate that the proposed BOA is more efficient than other metaheuristic algorithms.
Palvinder Singh Mann - One of the best experts on this subject based on the ideXlab platform.
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Improved artificial bee colony metaheuristic for energy-efficient clustering in wireless sensor networks
Artificial Intelligence Review, 2019Co-Authors: Palvinder Singh Mann, Satvir SinghAbstract:Energy-efficient clustering is a well known NP-hard optimization problem for complex and dynamic Wireless sensor networks (WSNs) environment. Swarm intelligence (SI) based metaheuristic like Ant colony optimization, Particle swarm optimization and more recently Artificial bee colony (ABC) has shown desirable properties of being adaptive to solve optimization problem of energy efficient clustering in WSNs. ABC arose much interest over other population-based Metaheuristics for solving optimization problems in WSNs due to ease of implementation however, its search equation contributes to its insufficiency due to poor exploitation phase and storage of certain control parameters. Thus, we propose an improved Artificial bee colony (iABC) metaheuristic with an improved search equation to enhance its exploitation capabilities and in order to increase the global convergence of the proposed metaheuristic, an improved population sampling technique is introduced through Student’s-t distribution, which require only one control parameter to compute and store, hence increase efficiency of proposed metaheuristic. The proposed metaheuristic maintain a good balance between exploration and exploitation search abilities with least memory requirements, moreover the use of first of its kind compact Student’s-t distribution, make it suitable for limited hardware requirements of WSNs. Further, an energy efficient bee clustering protocol based on iABC metaheuristic is introduced, which inherit the capabilities of the proposed metaheuristic to obtain optimal cluster heads and improve energy efficiency in WSNs. Simulation results show that the proposed clustering protocol outperforms other well known SI based protocols on the basis of packet delivery, throughput, energy consumption and extend network lifetime.
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Improved metaheuristic-based energy-efficient clustering protocol with optimal base station location in wireless sensor networks
Soft Computing, 2019Co-Authors: Palvinder Singh Mann, Satvir SinghAbstract:Efficient clustering is a well-documented NP-hard optimization problem in wireless sensor networks (WSNs). Variety of computational intelligence techniques including evolutionary algorithms, reinforcement learning, artificial immune systems and recently, artificial bee colony (ABC) metaheuristic have been applied for efficient clustering in WSNs. Due to ease of use and adaptive nature, ABC arose much interest over other population-based Metaheuristics for solving optimization problems in WSNs. However, its search equation contributes to its insufficiency due to comparably poor exploitation cycle and requirement of certain control parameters. Thus, we propose an improved artificial bee colony (iABC) metaheuristic with an improved solution search equation to improve exploitation capabilities of existing metaheuristic. Further, to enhance the global convergence of the proposed metaheuristic, an improved population sampling technique is introduced through Student’s t-distribution, which require only one control parameter to compute and store and therefore increase efficiency of proposed metaheuristic. The proposed metaheuristic maintain a good balance between exploration and exploitation search abilities with least memory requirements; moreover, the use of first-of-its-kind compact Student’s t-distribution makes it suitable for limited hardware requirements of WSNs. Additionally, an energy-efficient clustering protocol based on iABC metaheuristic is presented, which inherits the capabilities of the proposed metaheuristic to obtain optimal cluster heads along with an optimal base station location to improve energy efficiency in WSNs. Simulation results show that the proposed clustering protocol outperforms other well-known protocols on the basis of packet delivery, throughput, energy consumption, network lifetime and latency as performance metric.
Andrea Roli - One of the best experts on this subject based on the ideXlab platform.
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Metaheuristics for the Portfolio Selection Problem
2020Co-Authors: Giacomo Di Tollo, Andrea RoliAbstract:The Portfolio selection problem is a relevant problem arising in finance and economics. Some practical formulations of the problem include various kinds of nonlinear constraints and objectives and can be efficiently solved by approximate algorithms. Among the most effective approximate algorithms, are metaheuristic methods that have been proved to be very successful in many applications. This paper presents an overview of the literature on the application of Metaheuristics to the portfolio selection problem, trying to provide a general descriptive scheme. Keywords ─Metaheuristics, Local search, Portfolio selection, Portfolio optimization
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hybrid Metaheuristics in combinatorial optimization a survey
Applied Soft Computing, 2011Co-Authors: Christian Blum, Jakob Puchinger, Günther Raidl, Andrea RoliAbstract:Research in Metaheuristics for combinatorial optimization problems has lately experienced a noteworthy shift towards the hybridization of Metaheuristics with other techniques for optimization. At the same time, the focus of research has changed from being rather algorithm-oriented to being more problem-oriented. Nowadays the focus is on solving the problem at hand in the best way possible, rather than promoting a certain metaheuristic. This has led to an enormously fruitful cross-fertilization of different areas of optimization. This cross-fertilization is documented by a multitude of powerful hybrid algorithms that were obtained by combining components from several different optimization techniques. Hereby, hybridization is not restricted to the combination of different Metaheuristics but includes, for example, the combination of exact algorithms and Metaheuristics. In this work we provide a survey of some of the most important lines of hybridization. The literature review is accompanied by the presentation of illustrative examples.
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Hybrid Metaheuristics
Hybrid Optimization, 2010Co-Authors: Christian Blum, Jakob Puchinger, Günther Raidl, Andrea RoliAbstract:International audienceOne of the most interesting recent trends for what concerns research on Metaheuristics is their hybridization with other techniques for optimization. In fact, the focus of research on Metaheuristics has notably shifted from an algorithm-oriented point of view to a problem-oriented point of view. In other words, in contrast to promoting a certain metaheuristic, as, for example, in the eighties and the first half of the nineties, nowadays researchers focus much more on solving, as best as possible, the problem at hand. This has inevitably led to research that aims at combining different algorithmic components for the design of algorithms that are more powerful than the ones resulting from the implementation of pure metaheuristic strategies. Interestingly, the trend of hybridization is not restricted to the combination of algorithmic components originating from different Metaheuristics, but has also been extended to the combination of exact algorithms and Metaheuristics. In this chapter, we provide an overview of the most important lines of hybridization. In addition to representative examples, we present a literature review for each of the considered hybridization types
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hybrid Metaheuristics an emerging approach to optimization
Hybrid Metaheuristics: An Emerging Approach to Optimization 1st, 2008Co-Authors: Christian Blum, Andrea Roli, Maria Jos Blesa Aguilera, Michael SampelsAbstract:Optimization problems are of great importance in many fields. They can be tackled, for example, by approximate algorithms such as Metaheuristics. Examples of Metaheuristics are simulated annealing, tabu search, evolutionary computation, iterated local search, variable neighborhood search, and ant colony optimization. In recent years it has become evident that a skilled combination of a metaheuristic with other optimization techniques, a so called hybrid metaheuristic, can provide a more efficient behavior and a higher flexibility. This is because hybrid Metaheuristics combine their advantages with the complementary strengths of, for example, more classical optimization techniques such as branch and bound or dynamic programming. The authors involved in this book are among the top researchers in their domain. The book is intended both to provide an overview of hybrid Metaheuristics to novices of the field, and to provide researchers from the field with a collection of some of the most interesting recent developments.
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MAGMA: a multiagent architecture for Metaheuristics
IEEE transactions on systems man and cybernetics. Part B Cybernetics : a publication of the IEEE Systems Man and Cybernetics Society, 2004Co-Authors: Michela Milano, Andrea RoliAbstract:In this work, we introduce a multiagent architecture called the MultiAGent Metaheuristic Architecture (MAGMA) conceived as a conceptual and practical framework for metaheuristic algorithms. Metaheuristics can be seen as the result of the interaction among different kinds of agents: The basic architecture contains three levels, each hosting one or more agents. Level-0 agents build solutions, level-1 agents improve solutions, and level-2 agents provide the high level strategy. In this framework, classical metaheuristic algorithms can be smoothly accommodated and extended. The basic three level architecture can be enhanced with the introduction of a fourth level of agents (level-3 agents) coordinating lower level agents. With this additional level, MAGMA can also describe, in a uniform way, cooperative search and, in general, any combination of Metaheuristics. We describe the entire architecture, the structure of agents in each level in terms of tuples, and the structure of their coordination as a labeled transition system. We propose this perspective with the aim to achieve a better and clearer understanding of Metaheuristics, obtain hybrid algorithms, suggest guidelines for a software engineering-oriented implementation and for didactic purposes. Some specializations of the general architecture will be provided in order to show that existing Metaheuristics [e.g., greedy randomized adaptive procedure (GRASP), ant colony optimization (ACO), iterated local search (ILS), memetic algorithms (MAs)] can be easily described in our framework. We describe cooperative search and large neighborhood search (LNS) in the proposed framework exploiting level-3 agents. We show also that a simple hybrid algorithm, called guided restart ILS, can be easily conceived as a combination of existing components in our framework.
Ben Paechter - One of the best experts on this subject based on the ideXlab platform.
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a comparison of the performance of different Metaheuristics on the timetabling problem
Lecture Notes in Computer Science, 2003Co-Authors: Olivia Rossidorial, Marco Chiarandini, Monaldo Mastrolilli, Mauro Birattari, M Manfrin, Marco Dorigo, Michael Sampels, Luca Maria Gambardella, Joshua Knowles, Ben PaechterAbstract:The main goal of this paper is to attempt an unbiased comparison of the performance of straightforward implementations of five different Metaheuristics on a university course timetabling problem. In particular, the Metaheuristics under consideration are Evolutionary Algorithms, Ant Colony Optimization, Iterated Local Search, Simulated Annealing, and Tabu Search. To attempt fairness, the implementations of all the algorithms use a common solution representation, and a common neighbourhood structure or local search. The results show that no metaheuristic is best on all the timetabling instances considered. Moreover, even when instances are very similar, from the point of view of the instance generator, it is not possible to predict the best metaheuristic, even if some trends appear when focusing on particular instance classes. These results underline the difficulty of finding the best Metaheuristics even for very restricted classes of timetabling problem.
Nasser A. El-sherbeny - One of the best experts on this subject based on the ideXlab platform.
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Vehicle routing with time windows: An overview of exact, heuristic and metaheuristic methods
Journal of King Saud University - Science, 2010Co-Authors: Nasser A. El-sherbenyAbstract:In this paper, we present a review with some limited of exacts, heuristics and Metaheuristics methods for the vehicle routing problem time windows (VRPTW). Over the past 20. years vehicle routing problem with time windows has been an area of research that has attracted many researchers. In this period a number of papers have been published on the exact, heuristics and metaheursitics methods of the routing problem with time windows. This problem has model character in many branches of mathematics, computer science, and operations research. Metaheuristics support managers in decision-making with robust that provide high-quality solutions to important applications in business, engineering, economic and science in reasonable time horizons. © 2010.
