Socio-cognitive Evolution Strategies

Urbańczyk, Aleksandra; Nowak, B.; Orzechowski, Patryk; Moore, Jason H.; Kisiel-Dorohinicki, Marek; Byrski, Aleksander

doi:10.1007/978-3-030-77964-1_26

Cited by 4 publications

(5 citation statements)

References 27 publications

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“…The load on a cluster head 𝐿𝑜𝑎𝑑(𝐶𝐻𝑞) is influenced by the number of sensor nodes (𝑁), initial energy 𝐸 𝑖𝑛𝑖𝑡𝑖𝑎𝑙 (𝐶𝐻 𝑞 ) and remaining energy 𝐸 𝑟𝑒𝑚𝑎𝑖𝑛 (𝐶𝐻 𝑞 ) as described in Eqs. (25) and (26).…”

Section: Ch Selectionmentioning

confidence: 99%

“…𝐸 𝑖𝑛𝑖𝑡𝑖𝑎𝑙 (𝐶𝐻 𝑞 ) (25) The energy consumption of each node is modeled by the function 𝑓 2 = 𝐸 𝑇 (𝑁, 𝑑) where 𝑑 0 represents the distance between the sender and receiver, 𝑁 denotes the number of nodes, and 𝐸 𝑒𝑙𝑒𝑐 is the energy required by electronic circuitry.…”

Section: 𝐿𝑜𝑎𝑑 (𝐶𝐻 𝑞 ) = 𝑁 × 𝐸 𝑟𝑒𝑚𝑎𝑖𝑛 (𝐶𝐻 𝑞 )mentioning

confidence: 99%

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CCOA-DC: A Novel Optimization with NMF Data Compression in WSN Data Aggregation

2024

IJIES

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Wireless Sensor Networks (WSNs) play a pivotal role in remote monitoring, surveillance, and Internet of Things (IoT) applications. The efficient utilization of battery-powered sensor nodes in WSNs, given their limited power capacity, is crucial for successful data transmission. Conventional clustering algorithms while efficient in clustering, often lacks the efficient management of data generated by sensor nodes, leading to redundant data in applications like IoT leading to reduced network lifetime. To overcome this issue, this paper introduces a novel approach, named CCOA-DC (Improved Coati Optimization with Cognitive Factor (CCOA) through Data Compression (DC)), in clustering heterogeneous aggregated WSN data. The research unfolds in two novel phases. Initially, a non-negative matrix factorization (NMF) model is introduced data compression for clustering, addressing the challenge of data transmission and energy efficiency. Subsequently, the performance is enhanced through load balancing, featuring dynamic cluster head selection via Improved Coati Optimization (COA) with cognitive factor (C), denoted as CCOA. A distinctive aspect is the incorporation of the NMF data compression technique in both clustering and cluster head selection processes, introducing an energy-efficient, load-balanced, and compressed data aggregation mechanism. The proposed CCOA-DC undergoes rigorous testing, comparing its performance against existing models to validate its superiority. Comparative analyses with renowned models such as TCBDGA, HEED, and FEEC-IIR underscore the distinct advantages of CCOA-DC. Notably, it achieves a reduction of 78.57% of packet loss ratio compared to FEEC-IIR model. The model achieves high packet delivery ratio which is 98.67%, and shows optimized energy consumption of 68.01% Joules. This novel compression-based metaheuristic data aggregation algorithm showcases its effectiveness in addressing the energy conservation challenge, affirming its prominence in the area of WSNs based IoT applications.

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Section: Ch Selectionmentioning

confidence: 99%

Section: 𝐿𝑜𝑎𝑑 (𝐶𝐻 𝑞 ) = 𝑁 × 𝐸 𝑟𝑒𝑚𝑎𝑖𝑛 (𝐶𝐻 𝑞 )mentioning

confidence: 99%

CCOA-DC: A Novel Optimization with NMF Data Compression in WSN Data Aggregation

2024

IJIES

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“…As already mentioned in the Introduction, we root our work in a discourse of socio-cognitively inspired algorithms. The irst objective of introducing sociocognitive mechanism into evolution strategies served as a proof-of-concept that turned out to be promising [11], but pointed out several dimensions for major improvements. The irst conclusion was that these mechanisms that operate towards better solutions give better results than operators based on moving away from the worst individuals.…”

Section: Related Non-classic Evolutionary Algorithmsmentioning

confidence: 99%

“…We have decided to introduce the socio-cognitive mechanisms to the (𝜇 + 𝜆) version of ES. This follows from the apparent potential of such mechanisms developed earlier in [11]. We have studied the updating part of the operators applied therein, and introduced modi ications in order to increase their ef icacy.…”

Section: Socio-cognitive (𝝁 + 𝝀) Evolution Strategymentioning

confidence: 99%

“…The main contribution of this paper is a sociocognitively inspired mutation mechanism, that makes it possible to exchange the information among the individuals in evolutionary algorithms. The proof-ofconcept of this mechanism was introduced in the research paper in 2021 [11] and was redesigned and reimplemented based on the results achieved. The ef iciency and ef icacy of the new version of the algorithms are tested using well-known highdimensional, multimodal benchmark functions.…”

Section: Introductionmentioning

confidence: 99%

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(µ + λ) Evolution Strategy with Socio-cognitive Mutation

Urbańczyk,

Kucaba,

Wojtulewicz

et al. 2024

JAMRIS

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Socio-cognitive computing is a paradigm developed for the last several years in our research group. It consists of introducing mechanisms inspired by inter-individual learning and cognition into metaheuristics. Different versions of the paradigm have been successfully applied in hybridizing Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Genetic Algorithms, Differential Evolution, and Evolutionary Multi-agent System (EMAS) metaheuristics. In this paper, we have followed our previous experiences in order to propose a novel mutation based on sociocognitive mechanism and test it based on Evolution Strategy (ES). The newly constructed versions were applied to popular benchmarks and compared with their reference versions.

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