NichePSO and the Merging Subswarm Problem

Crane, Tyler; Ombuki-Berman, Beatrice M.; Engelbrecht, Andries P.

doi:10.1109/iscmi51676.2020.9311551

Cited by 3 publications

(3 citation statements)

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“…If a particle or a sub-swarm moves within the radius of another sub-swarm, it is absorbed by the latter. Despite its performance, NichePSO has been shown to lose its diversity over time because all sub-swarms may eventually fuse into a single large swarm [10]. In contrast, comparing NichePSO, our proposed method top-k PSO retains the communication network within the swarm, which enables enhanced exploration of the environment.…”

Section: Related Workmentioning

confidence: 95%

“…NichePSO could identify the first peak with similar probability compared top-k PSO, but due to the long simulation time, the sub-swarms of NichePSO exhibited a high probability of coalescing, causing the swarm to lose multiple tracked op-tima [10], and making the behavior of the swarm closer to that of the canonical PSO. In multi-modal problems such as in our case, this phenomenon is problematic, as shown in Tables 2 to 4, where NichePSO performed worse than both baseline methods when identifying second and subsequent peaks.…”

Section: Static Environmentmentioning

confidence: 99%

“…In GCPSO, only the best particle (i.e., argmax p i ∈P r(x(pi))) continues exploration of the environment while other particles update positions using canonical PSO, reducing the susceptibility of its particles to the local trap problem. NichePSO has a tendency to coalesce [10] causing the swarm to behave more like GCPSO, which limits its ability to find multiple peaks. In contrast, although a re-diversification strategy is included in top-k PSO at the swarm level, a single converging particle suffices to pre-vent its activation.…”

Section: Static Environmentmentioning

confidence: 99%

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An Extension of Particle Swarm Optimization to Identify Multiple Peaks using Re-diversification in Static and Dynamic Environments

Raharja,

Sugawara

2023

International Journal of Smart Computing and Artificial Intelligence

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We propose an extension of the particle swarm optimization (PSO) algorithm for each particle to store multiple global optima internally for identifying multiple (top-k) peaks in static and dynamic environments. We then applied this technique to search and rescue problems of rescuing potential survivors urgently in life-threatening disaster scenarios. With the rapid development of robotics and computer technology, aerial drones can be programmed to implement search algorithms that locate potential survivors and relay their positions to rescue teams. We model an environment of a disaster area with potential survivors using randomized bivariate normal distributions. We extended the Clerk-Kennedy PSO algorithm as top-k PSO by considering individual drones as particles, where each particle remembers a set of global optima to identify the top-k peaks. By comparing several other algorithms, including the canonical PSO, Clerk-Kennedy PSO, and NichePSO, we evaluated our proposed algorithm in static and dynamic environments. The experimental results show that the proposed algorithm was able to identify the top-k peaks (optima) with a higher success rate than the baseline methods, although the rate gradually decreased with increasing movement speed of the peaks in dynamic environments.

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Section: Related Workmentioning

confidence: 95%

Section: Static Environmentmentioning

confidence: 99%

Section: Static Environmentmentioning

confidence: 99%

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