Mutation: A New Operator in Gravitational Search Algorithm Using Fuzzy Controller

Kherabadi, Hossein Azadi; Mood, Sepehr Ebrahimi; Javidi, Mohammad Masoud

doi:10.1515/cait-2017-0006

Cited by 8 publications

(6 citation statements)

References 30 publications

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“…Gao et al, 2018 [54] Visual tracking with flower pollination heuristics Rodrigues et al, 2016 [55] Binary flower pollination algorithm Guan et al, 2019 [56] Double-floor corridor allocation Kherabadi et al, 2017 [57] Gravitational search algorithm in Fuzzy controllers Szentesi et al, 2021 [58] Process optimization for distribution logistics Bányai et al, 2017 [59] Optimization of blending technologies Hardai et al, 2021 [60] Logistics aspects of I4.0 Kundrák et al, 2019 [61] Efficiency improvement in manufacturing technologies This proposal Optimization of in-plant supply for matrix production…”

Section: Cyberphysicalmentioning

confidence: 99%

Optimization of Material Supply in Smart Manufacturing Environment: A Metaheuristic Approach for Matrix Production

Bányai

2021

Machines

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In the context of Industry 4.0, the matrix production developed by KUKA robotics represents a revolutionary solution for flexible manufacturing systems. Because of the adaptable and flexible manufacturing and material handling solutions, the design and control of these processes require new models and methods, especially from a real-time control point of view. Within the frame of this article, a new real-time optimization algorithm for in-plant material supply of smart manufacturing is proposed. After a systematic literature review, this paper describes a possible structure of the in-plant supply in matrix production environment. The mathematical model of the mentioned matrix production system is defined. The optimization problem of the described model is an integrated routing and scheduling problem, which is an NP-hard problem. The integrated routing and scheduling problem are solved with a hybrid multi-phase black hole and flower pollination-based metaheuristic algorithm. The computational results focusing on clustering and routing problems validate the model and evaluate its performance. The case studies show that matrix production is a suitable solution for smart manufacturing.

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Section: Cyberphysicalmentioning

confidence: 99%

Optimization of Material Supply in Smart Manufacturing Environment: A Metaheuristic Approach for Matrix Production

Bányai

2021

Machines

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“…In this paper, tournament size is defined as a time function to control the exploration and exploitation GSA capabilities in different algorithm iterations, as well as improve its performance. In the proposed algorithm, selection intensity and variance are controlled by the appropriate definition of tournament size, which is determined according to equation (17):…”

Section: Definition 4 (Selection Variance)mentioning

confidence: 99%

“…In [16], a new method was defined for mass calculation in GSA using sigma scaling and Boltzmann selection functions. Moreover, in [17], a new operator, called mutation, was added to the GSA to overcome the premature convergence problem in multimodal functions. Even though the GSA has high exploring capability, there are some problems, such as the GSA falling into local optima.…”

Section: Introductionmentioning

confidence: 99%

A modified gravitational search algorithm and its application in lifetimemaximization of wireless sensor networks

Mood¹,

Javidi²

2019

Turk J Elec Eng & Comp Sci

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Recently, academic communities and industrial sectors have been affected by significant advancements in wireless sensor networks (WSNs). Employing clustering methods is the dominant method to maximize the WSN's lifetime, which is considered to be a major issue. Metaheuristic algorithms have attracted wide attention in the research area of clustering. In this paper, first a novel nature-inspired optimization algorithm based on the gravitational search algorithm (GSA) is defined. To control the exploitation and exploration capabilities of this algorithm, along with calculating the masses value, the tournament selection method is employed. Tournament size, the parameter of this method, is computed automatically using a function during the computational process of the algorithm. The abilities of the algorithm are balanced using this problem-independent parameter. Therefore, the performance of the proposed algorithm is improved in this paper. Moreover, a modified GSA is applied to an energy-efficient clustering protocol for WSNs to minimize the objective function defining the compact clusters that have cluster heads with high energy. The proposed search algorithm is evaluated in terms of some standard test functions. The results suggest that this method has better performance than other state-of-the-art optimization algorithms. In addition, simulation results indicate that the proposed method for the clustering problem in WSNs has better performance on network lifetime and delivery data packets in BS than other popular clustering methods.

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“…It can be divided into three categories: i) Evolutionary Algorithms (EA), like Genetic Algorithm (GA) [8], A Differential Evolution Algorithm (DE) [9]. ii) physics-based algorithms, like gravitational search algorithm (GSA) [10],Harmony Search Algorithm [11] and Simulated Annealing [12].iii) Swarm intelligence (SI)-based algorithm, like Particle Swarm (PSO) [13],…”

Section: Introductionmentioning

confidence: 99%

Solving Suggested Problems using Grey Wolf Optimization

Issa¹,

Alaa²,

Abed³

2022

Proceedings of 2nd International Multi-Disciplinary Conference Theme: Integrated Sciences and Technologies, IMDC-IST 2021, 7-9

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In this research, the Grey Wolf Optimizer (GWO) algorithm was applied, which is a type of metaheuristic optimization algorithms that is important for identifying problems and finding ways to improve them. In 2014, Seyedali Mirjalili came up with this algorithm. Its social hierarchy is modeled after that of grey wolves in nature. These wolves live in groups consisting of( 5 -12) individuals. Wolves are divided into four levels, alpha represents the first level it is accountable for the manufacture of important resolutions for the peak like hunting, bedtime, wake up, etc. As for the second level in the hierarchy, it represents the beta wolf and is the advisor of the alpha. Beta can be the leader after the death of one of the alpha wolves. The third level represents delta, which follows the commands of alpha and beta. it is the dominant omega. omega represents the last level, which obeys all other wolves. Furthermore, the chief algorithm stages such as chasing, searching the prey, encircle and attack the prey were applied. GWO algorithm was tested on three benchmarks test functions using MATLAB R2014a. the results were confirmed by comparing GWO through another intelligent swarm algorithm like Particle Swarm Optimization (PSO) algorithm. results showed the superiority GWO in achieving better results and high convergence speed.

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Mutation: A New Operator in Gravitational Search Algorithm Using Fuzzy Controller

Cited by 8 publications

References 30 publications

Optimization of Material Supply in Smart Manufacturing Environment: A Metaheuristic Approach for Matrix Production

Optimization of Material Supply in Smart Manufacturing Environment: A Metaheuristic Approach for Matrix Production

A modified gravitational search algorithm and its application in lifetimemaximization of wireless sensor networks

Solving Suggested Problems using Grey Wolf Optimization

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