Identifying Top-k Peaks Using an Extended Particle Swarm Optimization Algorithm with Re-diversification Mechanism

Raharja, Stephen; Sugawara, Toshiharu

doi:10.1109/iiaiaai55812.2022.00079

Cited by 1 publication

(4 citation statements)

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“…Finally, although we previously reported an extended PSO to find multiple peaks [29], we also demonstrate that the proposed method can find multiple peaks in a dynamic environment where some peaks gradually move over time.…”

Section: Related Workmentioning

confidence: 64%

“…However, repeating the experiment sometimes resulted in NichePSO performing better in identifying the first peak compared to top-k PSO by a small margin, vice versa. For more experimental results in static environments, please refer to our conference paper [29].…”

Section: Experimental Results In Static Environmentmentioning

confidence: 99%

“…Figure 1: Top view of an environment with three peaks [29] In our model, time is considered as discrete and calculated in unit of ticks. Let P be the n particles of the PSO swarm, i.e., the set of particles, where n is a positive integer.…”

Section: Model Of Environmentmentioning

confidence: 99%

“…Adding all distribution functions yields the utility value of the mixed distribution functions r(x) of x ∈ V , which is defined as [29] given rescue or the expected number of survivors. If data regarding area importance in the environment are unavailable, all mi are equalized.…”

Section: Model Of 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: 64%

Section: Experimental Results In Static Environmentmentioning

confidence: 99%

Section: Model Of Environmentmentioning

confidence: 99%

Section: Model Of Environmentmentioning

confidence: 99%

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