Improved differential evolution based on multi-armed bandit for multimodal optimization problems

Agrawal, Suchitra; Tiwari, Aruna; Naik, Prathamesh; Srivastava, A. C.

doi:10.1007/s10489-021-02261-1

Cited by 9 publications

(4 citation statements)

References 47 publications

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“…Nowadays, Multi-Armed Bandit Algorithms have been used by people in many fields. Multi-Armed Bandit Algorithms can help people make effective and good decisions in many uncertain practical situations [4].…”

Section: Applications and Specific Aspects Of Multi-armed Bandit Algo...mentioning

confidence: 99%

Multi-Armed Bandit Algorithms: Analysis and Applications Across Domains

Zhang

2024

HSET

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This study provides an in-depth exploration of the pivotal role of Multi-Armed Bandit (MAB) Algorithms in decision-making across diverse sectors, focusing on their theoretical foundations, real-world applications, and empirical evidence. MAB Algorithms, metaphorically representing choices among various slot machine arms with different rewards, are crucial in optimizing decisions in uncertain settings by striking a balance between exploration and exploitation. It examines four principal algorithms—Greedy, Epsilon-Greedy, Upper Confidence Bound, and Thompson Sampling—each tailored for specific types of decision-making scenarios. Their applications are extensive, particularly in fields like recommendation systems, financial strategy formulation, and network security, where they enable adaptive learning and strategic optimization. In the context of the 5G era, MAB Algorithms are instrumental in effectively managing wireless network resources amidst dynamic conditions. This is further exemplified through empirical studies, such as research on decision-making under uncertainty, which demonstrate the algorithms' effectiveness in guiding choices in experimental setups. The paper highlights the growing importance and sophistication of MAB Algorithms, emphasizing their significant role in advancing human decision-making capabilities.

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Section: Applications and Specific Aspects Of Multi-armed Bandit Algo...mentioning

confidence: 99%

Multi-Armed Bandit Algorithms: Analysis and Applications Across Domains

Zhang

2024

HSET

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“…This OBL is used for determining the value of fitness function in order to find whether the opposite is predominant when compared to the current solution. It considers the opposite value of S Complement i ð Þ with respect to the real value S lb Th ,ub Th ½ , which is computed over "n" dimensions based on Equation (13).…”

Section: Obl Improved Goamentioning

confidence: 99%

“…However, majority of these algorithms are either good in exploitation or exploration, but not both. In this situation, hybridizing two different metaheuristic algorithms in which the first one is superior in exploitation and the second one potential in exploration aids in maintaining better balance between local and global search tendencies 13 . Hence, hybridized metaheuristic algorithms‐based CH selection approach is considered to be highly appropriate for guaranteeing energy stability and network lifetime during the clustering process by sustaining the trade‐off between exploration and exploitation.…”

Section: Introductionmentioning

confidence: 99%

“…In this situation, hybridizing two different metaheuristic algorithms in which the first one is superior in exploitation and the second one potential in exploration aids in maintaining better balance between local and global search tendencies. 13 Hence, hybridized metaheuristic algorithms-based CH selection approach is considered to be highly appropriate for guaranteeing energy stability and network lifetime during the clustering process by sustaining the trade-off between exploration and exploitation. However, the existing hybridized metaheuristic algorithm-based CH selection is still recognized to have a room of improvement in balancing the exploration and exploitation, which in turn is responsible for augmenting network lifetime and energy stability.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid grasshopper and differential evolution algorithm for prolonging network life expectancy in wireless sensor networks (WSNs)

Manoharan¹,

Ponnusamy²

2022

Int J Communication

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In wireless sensor networks (WSNs), restricted battery power, balanced energy consumption, and collaborative data processing are considered as key challenges that need to be handled with utmost care. Clustering in WSNs is an optimal methodology for conserving energy of sensor nodes and attaining efficient data processing that attributes toward the maximization of network lifetime. An efficient cluster head (CH) selection scheme is essential for achieving superior collaborative data processing in WSNs. Swarm intelligent metaheuristic algorithm-based CH selection approaches are identified to be better for designing energy-efficient schemes that select optimal CHs from nodes in a fair way. In this paper, Hybrid Grasshopper and Differential Evolution-based Optimization Algorithm (HGDEOA) is proposed for targeting on the objective of attaining energy stability and prolonging network lifetime. This HGDEOA incorporates an adaptive strategy into differential evolution (DE) for improving the global searching capability during the process of optimization. It is proposed for improving the convergence efficiency and maintaining population diversity. It also possesses the capability of enhancing the degree of convergence speed and precision in calculation. This integration of GOA into DE prevents the premature convergence of the algorithm, since the deviation amid the scaling individuals induces the population to be more randomly distributed, targeting at the retention of population diversity. The simulation results confirm that the proposed HGDEOA sustains residual energy by 19.32%, improves throughput by 16.21%, prolongs network lifetime by 18.76%, and maintains stability by18.94% when compared to the benchmarked approaches used for investigation.

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