Empirical Assessment of Human Learning Principles Inspired PSO Algorithms on Continuous Black-Box Optimization Testbed

Tanweer, Muhammad Rizwan; Al-Dujaili, Abdullah; Sundaram, Suresh

doi:10.1007/978-3-319-48959-9_2

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…In this regard, hybridization [ 31 ] of for instance Social Learning Optimization inspired Archimedes Optimization Algorithm or a novel PSO model based on Simulating Cohort Intelligence. Recently human intelligences or human social communication based PSO models are developed and provided outstanding results [ 15 , 32 , 33 ]. Though t -entropy generated acceptable result, however, it could not be proved commendable when compared with the other objective functions under similar circumstances.…”

Section: Conclusion and Future Research Directionsmentioning

confidence: 99%

Section: Conclusion and Future Research Directionsmentioning

confidence: 99%

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Human-Inspired Optimization Algorithms: Theoretical Foundations, Algorithms, Open-Research Issues and Application for Multi-Level Thresholding

Rai

Das

Ray

et al. 2022

Arch Computat Methods Eng

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Humans take immense pride in their ability to be unpredictably intelligent and despite huge advances in science over the past century; our understanding about human brain is still far from complete. In general, human being acquire the high echelon of intelligence with the ability to understand, reason, recognize, learn, innovate, retain information, make decision, communicate and further solve problem. Thereby, integrating the intelligence of human to develop the optimization technique using the human problem-solving ability would definitely take the scenario to next level thus promising an affluent solution to the real world optimization issues. However, human behavior and evolution empowers human to progress or acclimatize with their environments at rates that exceed that of other nature based evolution namely swarm, bio-inspired, plant-based or physics-chemistry based thus commencing yet additional detachment of Nature-Inspired Optimization Algorithm (NIOA) i.e. Human-Inspired Optimization Algorithms ( HIOAs ). Announcing new meta-heuristic optimization algorithms are at all times a welcome step in the research field provided it intends to address problems effectively and quickly. The family of HIOA is expanding rapidly making it difficult for the researcher to select the appropriate HIOA; moreover, in order to map the problems alongside HIOA, it requires proper understanding of the theoretical fundamental, major rules governing HIOAs as well as common structure of HIOAs. Common challenges and open research issues are yet another important concern in HIOA that needs to be addressed carefully. With this in mind, our work distinguishes HIOAs on the basis of a range of criteria and discusses the building blocks of various algorithms to achieve aforementioned objectives. Further, this paper intends to deliver an acquainted survey and analysis associated with modern compartment of NIOA engineered upon the perception of human behavior and intelligence i.e. Human-Inspired Optimization Algorithms ( HIOAs ) stressing on its theoretical foundations, applications, open research issues and their implications on color satellite image segmentation to further develop Multi-Level Thresholding (MLT) models utilizing Tsallis and t-entropy as objective functions to judge their efficacy.

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