Improved Whale Optimization Algorithm Case Study: Clinical Data of Anaemic Pregnant Woman

Saidala, Ravi Kumar; Devarakonda, Nagaraju

doi:10.1007/978-981-10-3223-3_25

Cited by 26 publications

(15 citation statements)

References 8 publications

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“…The limitations of WOA are summarized as: WOA exhibits poor convergence speed in both exploration and exploitation phases as these two metrics depend on a single parameter, which is a’ [7] . It is essential to improve the balancing between exploration and exploitation in WOA [55] . The failure in fine tuning of exploitation and exploration makes WOA inferior than HHO, as in this manuscript, and results in local optima entrapment.…”

Section: Resultsmentioning

confidence: 99%

Optimizing multiple ONUs placement in Fiber-Wireless (FiWi) access network using Grasshopper and Harris Hawks Optimization Algorithms

Singh

Prakash

2020

Optical Fiber Technology

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Section: Resultsmentioning

confidence: 99%

Optimizing multiple ONUs placement in Fiber-Wireless (FiWi) access network using Grasshopper and Harris Hawks Optimization Algorithms

Singh

Prakash

2020

Optical Fiber Technology

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“…This parameter has a significant impact on WOA’s efficiency [ 66 ]. As a result of the mentioned factors, we discovered that WOA has a slow convergence rate during both the exploration and exploitation phases [ 67 ].…”

Section: Discussionmentioning

confidence: 99%

Optimizing hyperparameters of deep reinforcement learning for autonomous driving based on whale optimization algorithm

et al. 2021

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Deep Reinforcement Learning (DRL) enables agents to make decisions based on a well-designed reward function that suites a particular environment without any prior knowledge related to a given environment. The adaptation of hyperparameters has a great impact on the overall learning process and the learning processing times. Hyperparameters should be accurately estimated while training DRL algorithms, which is one of the key challenges that we attempt to address. This paper employs a swarm-based optimization algorithm, namely the Whale Optimization Algorithm (WOA), for optimizing the hyperparameters of the Deep Deterministic Policy Gradient (DDPG) algorithm to achieve the optimum control strategy in an autonomous driving control problem. DDPG is capable of handling complex environments, which contain continuous spaces for actions. To evaluate the proposed algorithm, the Open Racing Car Simulator (TORCS), a realistic autonomous driving simulation environment, was chosen to its ease of design and implementation. Using TORCS, the DDPG agent with optimized hyperparameters was compared with a DDPG agent with reference hyperparameters. The experimental results showed that the DDPG’s hyperparameters optimization leads to maximizing the total rewards, along with testing episodes and maintaining a stable driving policy.

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“…Found that WOA perform efficient than many state-of-the-art optimization algorithms including PSO [28]. Authors in [29] improved the optimization performance by introducing new convergence factor in both exploration and exploitation phase. Authors [30] proposed a new parallel metaheuristic optimization algorithm (NPMOA) which was formulated by hybridizing WOA with CSA.…”

Section: Related Workmentioning

confidence: 99%

Multi-Swarm Whale Optimization Algorithm for Data Clustering Problems using Multiple Cooperative Strategies

Saidala¹,

Devarakonda²

2018

IJISA

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Abstract-Computational Intelligence (CI) is an as of emerging area in addressing complex real world problems. The WOA has taken its root from the collective intelligent foraging behavior of humpback whales (Megaptera Novaeangliae). The standard WOA is suffers from the selection of best agent while whales searching and encircling prey. This research paper deals with the multi-swarm cooperative strategies for finding the best agents which balances the two phase's exploration and exploitation. The performance of invoked Multi-Swarm cooperative strategies into standard WOA i.e, MsWOA is first benchmarked on a set of 23 standard mathematical benchmark function problems which includes 7 UniModal, 6 Multi-modal and 10 fixed dimension multimodal functions. The obtained graphical and statistical results have been portrayed along with the previously established techniques. The obtained results depicts that the proposed cooperative strategies for WOA outperforms in solving optimization problems of standard benchmark functions. This paper also studies the numerical efficiency of proposed techniques on the problem of data clustering where 10 real data clustering problems have been taken from data repository https://archive.ics.uci.edu.data. Statistical results for the obtained cluster centroids, intra-cluster distances and inter-cluster distances confirms that the cooperative strategies for best whale agent selection improves the performance WOA for function optimization problems as well as data clustering problems.

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Improved Whale Optimization Algorithm Case Study: Clinical Data of Anaemic Pregnant Woman

Cited by 26 publications

References 8 publications

Optimizing multiple ONUs placement in Fiber-Wireless (FiWi) access network using Grasshopper and Harris Hawks Optimization Algorithms

Optimizing multiple ONUs placement in Fiber-Wireless (FiWi) access network using Grasshopper and Harris Hawks Optimization Algorithms

Optimizing hyperparameters of deep reinforcement learning for autonomous driving based on whale optimization algorithm

Multi-Swarm Whale Optimization Algorithm for Data Clustering Problems using Multiple Cooperative Strategies

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