Sperm Swarm Optimization Algorithm for Optimizing Wireless Sensor Network Challenges

Shehadeh, Hisham A.; Ahmedy, Ismail; Idris, Mohd Yamani Idna

doi:10.1145/3193092.3193100

Cited by 39 publications

(25 citation statements)

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“…Swarm-based metaheuristics are mostly inspired by behaviours of animals and plants which are capable of interacting with each other and with the environment. Examples of swarm-based metaheuristics are "Particle Swarm Optimization (PSO)" [12], "Sperm Swarm Optimization (SSO)" [13], and "Grey Wolf Optimizer (GWO)" [14]. Evolutionary-based metaheuristics are the techniques developed based on the laws of natural evolution.…”

Section: Literature Reviewmentioning

confidence: 99%

“…So, it can be said that competitive methods are methods that compete within the metaheuristic methods to achieve its goal which are mostly non-hybrid algorithms. For example, SSO is a competitive method where in SSO the sperms compete to reach the ovum in an optimal way [13]. In a recent work, a cooperative metaheuristic algorithm for global optimization problems was proposed [45].…”

Section: Literature Reviewmentioning

confidence: 99%

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A Hybrid Sperm Swarm Optimization and Genetic Algorithm for Unimodal and Multimodal Optimization Problems

et al. 2022

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A good exploration ability can ensure that the method jumps out of local optimum in multimodal problems and a good exploitation can ensure an algorithm converge faster to global optimum values. So, this paper proposes a new hybrid sperm swarm optimization and genetic algorithm to obtain global optimal solutions termed HSSOGA which is developed based on the concept of balancing the exploration and exploitation capability by merging Sperm Swarm Optimization (SSO), which has a fast convergence rate, and a Genetic Algorithm (GA) that can explore a search domain efficiently. To ensure that the proposed method delivers good performance, it is evaluated with 11 standard test function problems consisting of 5 unimodal and 6 multimodal functions. The proposed HSSOGA set is compared with conventional GA and SSO methods, as well as with several hybrid methods such as Hybrid Firefly and Particle Swarm Optimization (HFPSO), hybrid Simulated Annealing and Genetic Algorithm (SAGA), Hybrid Particle Swarm Optimization and Genetic Algorithm (HFPSO), hybrid Particle Swarm Optimization and Grey Wolf Optimization (PSOGWO), and closely related Hybrid Sperm Swarm Optimization and Gravitational Search Algorithm (HSSOGSA). The results are evaluated in terms of each method's best fitness, mean, standard deviation, and convergence rates. The numerical experiment results show that HSSOGA has better convergence towards the true global optimum values as compared to the conventional and existing hybrid methods in most unimodal and multimodal test function problems.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Sperm Swarm Optimization and Genetic Algorithm for Unimodal and Multimodal Optimization Problems

et al. 2022

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“…The GA performs a set of natural operations, including, different types of natural selection, crossover, and mutation to create a better generation [36]. In a different view, our algorithm Sperm Swarm Optimization (SSO) algorithm is a novel single objective optimization algorithm developed based on a metaphor of a natural fertilization procedure, which simulates the motility of sperm swarm through the fertilization procedure [37]. SSO is inherently continuous technique of updating the position and velocity of each sperm on search space domain until reaching the optimal solution [19,37].…”

Section: Multi-objective Optimization Algorithmsmentioning

confidence: 99%

“…The Memetic Algorithms (MAs) use GA operations namely, ranking, natural selection, crossover, and mutation operations with the addition of local search [41,42]. The comparison between SSO, MOSFP, PSO, OMOPSO, GA, NSGA-II, SPEA2 and MA (Hybrid GA) are summarized in Table 3 [16][17][18][19]37,[40][41][42][43]. Darwinian's theory of evolution applied to biology, which simulates the construction of chromosome and its evolution.…”

Section: Multi-objective Optimization Algorithmsmentioning

confidence: 99%

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The Multi-Objective Optimization Algorithm Based on Sperm Fertilization Procedure (MOSFP) Method for Solving Wireless Sensor Networks Optimization Problems in Smart Grid Applications

et al. 2018

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Prior studies in Wireless Sensor Network (WSN) optimization mostly concentrate on maximizing network coverage and minimizing network energy consumption. However, there are other factors that could affect the WSN Quality of Service (QoS). In this paper, four objective functions that affect WSN QoS, namely end-to-end delay, end-to-end latency, network throughput and energy efficiency are studied. Optimal value of packet payload size that is able to minimize the end-to-end delay and end-to-end latency, while also maximizing the network throughput and energy efficiency is sought. To do this, a smart grid application case study together with a WSN QoS model is used to find the optimal value of the packet payload size. Our proposed method, named Multi-Objective Optimization Algorithm Based on Sperm Fertilization Procedure (MOSFP), along with other three state-of-the-art multi-objective optimization algorithms known as OMOPSO, NSGA-II and SPEA2, are utilized in this study. Different packet payload sizes are supplied to the algorithms and their optimal value is derived. From the experiments, the knee point and the intersection point of all the obtained Pareto fronts for all the algorithms show that the optimal packet payload size that manages the trade-offs between the four objective functions is equal to 45 bytes. The results also show that the performance of our proposed MOSFP method is highly competitive and found to have the best average value compared to the other three algorithms. Furthermore, the overall performance of MOSFP on four objective functions outperformed OMOPSO, NSGA-II and SPEA2 by 3%, 6% and 51%, respectively.

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A Hybrid Grey Wolf Optimizer and Sperm Swarm Optimization for Global Optimization

Shehadeh

Shagari

2022

Handbook of Intelligent Computing and Optimization for Sustainable Development

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Sperm Swarm Optimization Algorithm for Optimizing Wireless Sensor Network Challenges

Cited by 39 publications

References 20 publications

A Hybrid Sperm Swarm Optimization and Genetic Algorithm for Unimodal and Multimodal Optimization Problems

A Hybrid Sperm Swarm Optimization and Genetic Algorithm for Unimodal and Multimodal Optimization Problems

The Multi-Objective Optimization Algorithm Based on Sperm Fertilization Procedure (MOSFP) Method for Solving Wireless Sensor Networks Optimization Problems in Smart Grid Applications

A Hybrid Grey Wolf Optimizer and Sperm Swarm Optimization for Global Optimization

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