Swarm Optimization for Energy-Based Acoustic Source Localization: A Comprehensive Study

Abstract

In the last decades, several swarm-based optimization algorithms have emerged in the scientific literature, followed by a massive increase in terms of their fields of application. Most of the studies and comparisons are restricted to high-level languages (such as MATLAB®) and testing methods on classical benchmark mathematical functions. Specifically, the employment of swarm-based methods for solving energy-based acoustic localization problems is still in its inception and has not yet been extensively studied. As such, the present work marks the first comprehensive study of swarm-based optimization algorithms applied to the energy-based acoustic localization problem. To this end, a total of 10 different algorithms were subjected to an extensive set of simulations with the following aims: (1) to compare the algorithms’ convergence performance and recognize novel, promising methods for solving the problem of interest; (2) to validate the importance (in convergence speed) of an intelligent swarm initialization for any swarm-based algorithm; (3) to analyze the methods’ time efficiency when implemented in low-level languages and when executed on embedded processors. The obtained results disclose the high potential of some of the considered swarm-based optimization algorithms for the problem under study, showing that these methods can accurately locate acoustic sources with low latency and bandwidth requirements, making them highly attractive for edge computing paradigms.

An ameliorated localization algorithm for compensating stratification effect based on improved underwater salp swarm optimization technique

“…The localization of the randomly distributed unknown nodes is performed using the proposed algorithm (LAS-IUSSOT). Then, the values are computed based on the minimization of the objective function stated in Equations (10) and (11). Initially, in 3D-UASN, every node such as target, beacon, and reinforced nodes are haphazardly distributed.…”

“…3. Then, compute the position of unknown nodes using Equation (11), if d ju is greater than d jv ; otherwise, employ Equation (10). 4.…”

“…The traditional salp swarm intelligence has a drawback: it easily gets trapped in a local optimum solution. This drawback is eliminated in the proposed IUSSOT 11–15 . Figure 1 illustrates the impediments of three‐dimensional underwater acoustic sensor network (3D‐UASN).…”

“…This drawback is eliminated in the proposed IUSSOT. [11][12][13][14][15] Figure 1 illustrates the impediments of threedimensional underwater acoustic sensor network (3D-UASN). Impediments are challenges addressed in 3D-UASN 1,4,16,17 while computing the position of underwater sensor nodes.…”

Firefly Optimization Heuristics for Sustainable Estimation in Power System Harmonics

“…The concept of population search based optimization through swarm/evolutionary heuristics has been emerged over the recent years with excellent performance in solving various engineering and applied sciences problems [21][22][23][24][25]. The metaheuristics are also exploited for optimization of power systems [26][27][28][29][30], as well as harmonics estimation, such as, neuro evolutionary approach [31], evolutionary technique [32], grey wolf optimizer [33], particle swarm optimizer (PSO) [34][35][36], artificial bee colony [37][38][39], biogeography based optimization [40], fractional order PSO [41] and cuckoo search optimization [42].…”