Swarm Intelligence Research: From Bio-inspired Single-population Swarm Intelligence to Human-machine Hybrid Swarm Intelligence

Wang, Guoyin; Cheng, Dongdong; Xia, Deyou; Jiang, Hai-Huan

doi:10.1007/s11633-022-1367-7

Cited by 14 publications

(6 citation statements)

References 197 publications

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“…This interdisciplinary nexus is exemplified by initiatives such as the Interspecies Internet (Dolgin, 2019;Jones, 2019) and the use of environmental sensing technologies (Gabrys and Pritchard, 2018), which are pioneering the way for a new form of artistic expression and communication across species and technologies. This blending of human creativity, animal behaviour, and technological intervention serves as a powerful testament to the potential of collective intelligence and creativity (Bonnet et al, 2019;Wang et al, 2023). It suggests that authorship can extend beyond the confines of human endeavour, encompassing the contributions of non-human participants whose interactions with technology provide a unique perspective to transcend creativity.…”

Section: Photons Be Freementioning

confidence: 99%

Exploration of the creative processes in animals, robots, and AI: who holds the authorship?

Sueur,

Lombard,

Capra

et al. 2024

Humanit Soc Sci Commun

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Picture a simple scenario: a worm, in its modest way, traces a trail of paint as it moves across a sheet of paper. Now shift your imagination to a more complex scene, where a chimpanzee paints on another sheet of paper. A simple question arises: Do you perceive an identical creative process in these two animals? Can both of these animals be designated as authors of their creation? If only one, which one? This paper delves into the complexities of authorship, consciousness, and agency, unpacking the nuanced distinctions between such scenarios and exploring the underlying principles that define creative authorship across different forms of life. It becomes evident that attributing authorship to an animal hinges on its intention to create, an aspect intertwined with its agency and awareness of the creative act. These concepts are far from straightforward, as they traverse the complex landscapes of animal ethics and law. But our exploration does not stop there. Now imagine a robot, endowed with artificial intelligence, producing music. This prompts us to question how we should evaluate and perceive such creations. Is the creative process of a machine fundamentally different from that of an animal or a human? As we venture further into this realm of human-made intelligence, we confront an array of ethical, philosophical, and legal quandaries. This paper provides a platform for a reflective discussion: ethologists, neuroscientists, philosophers, and bioinformaticians converge in a multidisciplinary dialogue. Their insights provide valuable perspectives for establishing a foundation upon which to discuss the intricate concepts of authorship and appropriation concerning artistic works generated by non-human entities.

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Section: Photons Be Freementioning

confidence: 99%

Exploration of the creative processes in animals, robots, and AI: who holds the authorship?

Sueur,

Lombard,

Capra

et al. 2024

Humanit Soc Sci Commun

View full text Add to dashboard Cite

show abstract

“…Bio-inspired single-population swarm intelligence and human–machine hybrid swarm intelligence can serve as new avenues for exploration in the optimization of multi-robot systems (Wang et al , 2023). This includes algorithms such as GA (Cheng and Meng, 2023) and differential evolution algorithm.…”

Section: Energy Optimization For Autonomous Mobile Robotsmentioning

confidence: 99%

A review on energy efficiency in autonomous mobile robots

Wu,

Yeong,

et al. 2023

RIA

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Purpose This paper aims to provide a comprehensive analysis of the state of the art in energy efficiency for autonomous mobile robots (AMRs), focusing on energy sources, consumption models, energy-efficient locomotion, hardware energy consumption, optimization in path planning and scheduling methods, and to suggest future research directions. Design/methodology/approach The systematic literature review (SLR) identified 244 papers for analysis. Research articles published from 2010 onwards were searched in databases including Google Scholar, ScienceDirect and Scopus using keywords and search criteria related to energy and power management in various robotic systems. Findings The review highlights the following key findings: batteries are the primary energy source for AMRs, with advances in battery management systems enhancing efficiency; hybrid models offer superior accuracy and robustness; locomotion contributes over 50% of a mobile robot’s total energy consumption, emphasizing the need for optimized control methods; factors such as the center of mass impact AMR energy consumption; path planning algorithms and scheduling methods are essential for energy optimization, with algorithm choice depending on specific requirements and constraints. Research limitations/implications The review concentrates on wheeled robots, excluding walking ones. Future work should improve consumption models, explore optimization methods, examine artificial intelligence/machine learning roles and assess energy efficiency trade-offs. Originality/value This paper provides a comprehensive analysis of energy efficiency in AMRs, highlighting the key findings from the SLR and suggests future research directions for further advancements in this field.

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“…Recent research has examined several facets of Swarm Intelligence, including underwater robotic swarms, multirobot systems, and the application of simulation models to the fields of firm search behaviour and swarm robotics. The three-layer hierarchical research approach for bio-inspired swarm intelligence and human-machine hybrid swarm intelligence is described in the proposed research in Wang et al (2023). The system in Kwa et al (2023) is composed of autonomous agents that possess emergent intellectual capabilities.…”

Section: Introductionmentioning

confidence: 99%

Lizard-Moth Flame Optimization Algorithm for Large-Scale Global Optimization Problems

Singh,

Sing,

Chaurasia

et al. 2024

Preprint

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This paper introduces a disruptive strategy, namely a lizard-hunting approach, into the classical Moth Flame Optimization (MFO) algorithm. The conventional MFO emulates a moth's navigation pattern around artificial light at night but tends to face stagnation due to the flame's exploitative tendencies, often getting trapped in local optima, particularly in higher-dimensional problems. The research motivation stems from the need to disrupt small groups stuck at various local optima after a certain number of iterations. To address the limitations of the existing MFO, the proposed Lizard-Moth-Flame Optimization (L-MFO) algorithm is put forth. In L-MFO, moth positions are classified into outlier and non-outlier categories using a clustering method in each iteration. Following this categorization, non-outliers are divided into highly and less densely populated subgroups, with the densely crowded group considered closer to the solution. However, a distinctive aspect of the lizard's behaviour in L-MFO is its inclination towards the less crowded group, reflecting a slower update. When a moth detects a lizard within its range and at the same angle, moths within the group either flee in the opposite direction or move towards a densely crowded group. This strategic response mitigates the issue of stagnation, enhancing the algorithm's overall performance. The proposed L-MFO algorithm undergoes a comprehensive evaluation by being compared with other state-of-the-art meta-heuristic algorithms. The assessment involves testing on twenty-three CEC-2005 benchmarks across different dimensions (10, 30, 50, 100, 500, 1000, 2000, and 5000), eight engineering problems, and 36 CEC-2017 benchmark functions with 10, 50 and 100 dimensions. The robustness of the algorithm is examined through convergence and divergence analysis, Wilcoxon signed-rank test, Two-tailed Mann-Whitney U test, and boxplot analysis. The experimental and statistical results consistently demonstrate the superior performance of L-MFO over other algorithms.

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Swarm Intelligence Research: From Bio-inspired Single-population Swarm Intelligence to Human-machine Hybrid Swarm Intelligence

Cited by 14 publications

References 197 publications

Exploration of the creative processes in animals, robots, and AI: who holds the authorship?

Exploration of the creative processes in animals, robots, and AI: who holds the authorship?

A review on energy efficiency in autonomous mobile robots

Lizard-Moth Flame Optimization Algorithm for Large-Scale Global Optimization Problems

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