A novel hybrid meta-heuristic technique applied to the well-known benchmark optimization problems

Abtahi, Amir-Reza; Bijari, Afsane

doi:10.1007/s40092-016-0170-x

Cited by 7 publications

(7 citation statements)

References 31 publications

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“…Further, there are plenty of real-world problems that may be challenging to solve with limited resources of time. Even though the traditional methods of mathematical programming are ideal to deal with problems of a small-scale and is also posed to solve instances of a large-scale for the Non-Deterministic Polynomial (NP)-hard problems [7]. There have been plenty of metaheuristic algorithms working with several mechanisms inspired from the physical, mechanical, cultural, political, economic, social and natural concepts.…”

Section: Sathish K Srinivasanmentioning

confidence: 99%

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Efficient Localized Data Aggregation of IoT Devices and its Application in Agriculture

2020

IJRTE

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Abstract: The Internet of Things (IoT) has a likely future for all technologies that are associated to the life of humans. Any communication between the social environments along with the user contexts will be made through the smart interfaces. The IoT will have to link to different diverse devices found in the Wireless Sensor Network (WSN) . Thus, the routing optimization which is energy efficient has become the primary factor in the performance of the network in the IoT. The widely popular routing used in WSN, Multi-hop Low Energy Adaptive Clustering Hierarchy (LEACH) protocol, is found to be energy inefficient. The work will deal with the choice of finding the optimal path in routing through meta heuristic techniques to improve the lifespan of the network and the energy efficiency of the network. There are different techniques of metaheuristics such as Teacher Learning Based Optimization (TLBO) andParticle Swarm Optimization (PSO) were used effectively for finding optimal solutions for problems. In this work, the PSO-based algorithms were used for locating an optimal sink position to their nodes that make the network efficient in terms of energy. The TLBO metaheuristic is population-based and is based on the concept of teaching and the learning procedure observed in a classroom, which is adapted to the routing problem. The results of the experiment prove that the proposed technique was achieved better levels of performance compared to the other methods..

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Section: Sathish K Srinivasanmentioning

confidence: 99%

“…Choose two learners, P and Q so that (7) Now g to step (3) for the subsequent iteration and repeat until such time a predefined number of generations are met or this is done after a certain acceptable solution is found…”

Section: Teaching-learning-based Optimization (Tlbo) Algorithmmentioning

confidence: 99%

Efficient Localized Data Aggregation of IoT Devices and its Application in Agriculture

2020

IJRTE

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“…Glover et al (1999) presented the successful development of OptQuest, an optimization tool box containing different algorithms (mainly metaheuristics) designed to optimize configuration decisions in simulation models. OptQuest uses iterative heuristics (Kleijnen 2008), and it can be used to utilize a combination of three meta-heuristics: Scatter Search (SS), Tabu Search (TS), and Neural Networks (NN) (Glover et al 1999;Keskin et al 2010;Abtahi and Bijari 2017). An example of successful application of scatter search within OptQuest is reported by Bulut (2001), to solve a multi-scenario optimization problem based on a large-scale linear programming problem.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal design of supply chain network under uncertainty environment using hybrid analytical and simulation modeling approach

Chiadamrong

Piyathanavong

2017

J Ind Eng Int

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Models that aim to optimize the design of supply chain networks have gained more interest in the supply chain literature. Mixed-integer linear programming and discrete-event simulation are widely used for such an optimization problem. We present a hybrid approach to support decisions for supply chain network design using a combination of analytical and discrete-event simulation models. The proposed approach is based on iterative procedures until the difference between subsequent solutions satisfies the pre-determined termination criteria. The effectiveness of proposed approach is illustrated by an example, which shows closer to optimal results with much faster solving time than the results obtained from the conventional simulation-based optimization model. The efficacy of this proposed hybrid approach is promising and can be applied as a powerful tool in designing a real supply chain network. It also provides the possibility to model and solve more realistic problems, which incorporate dynamism and uncertainty.

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“…Meta-heuristics are popular optimization algorithms, often used to solve large-scale complex optimization problems in various fields (Abtahi and Bijari 2016;Javanmard and Koraeizadeh 2016;Moradgholi et al 2016). In the research of Shao et al (2009), a genetic algorithmbased method was used for optimization of two functions: process planning and scheduling.…”

Section: Literature Reviewmentioning

confidence: 99%

An improved genetic algorithm for multidimensional optimization of precedence-constrained production planning and scheduling

2017

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Integration of production planning and scheduling is a class of problems commonly found in manufacturing industry. This class of problems associated with precedence constraint has been previously modeled and optimized by the authors, in which, it requires a multidimensional optimization at the same time: what to make, how many to make, where to make and the order to make. It is a combinatorial, NP-hard problem, for which no polynomial time algorithm is known to produce an optimal result on a random graph. In this paper, the further development of Genetic Algorithm (GA) for this integrated optimization is presented. Because of the dynamic nature of the problem, the size of its solution is variable. To deal with this variability and find an optimal solution to the problem, GA with new features in chromosome encoding, crossover, mutation, selection as well as algorithm structure is developed herein. With the proposed structure, the proposed GA is able to ''learn'' from its experience. Robustness of the proposed GA is demonstrated by a complex numerical example in which performance of the proposed GA is compared with those of three commercial optimization solvers.

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A novel hybrid meta-heuristic technique applied to the well-known benchmark optimization problems

Cited by 7 publications

References 31 publications

Efficient Localized Data Aggregation of IoT Devices and its Application in Agriculture

Efficient Localized Data Aggregation of IoT Devices and its Application in Agriculture

Optimal design of supply chain network under uncertainty environment using hybrid analytical and simulation modeling approach

An improved genetic algorithm for multidimensional optimization of precedence-constrained production planning and scheduling

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