An Immune Cooperative Particle Swarm Optimization Algorithm for Fault‐Tolerant Routing Optimization in Heterogeneous Wireless Sensor Networks

Abstract

The fault-tolerant routing problem is important consideration in the design of heterogeneous wireless sensor networks (H-WSNs) applications, and has recently been attracting growing research interests. In order to maintainkdisjoint communication paths from source sensors to the macronodes, we present a hybrid routing scheme and model, in which multiple paths are calculated and maintained in advance, and alternate paths are created once the previous routing is broken. Then, we propose an immune cooperative particle swarm optimization algorithm (ICPSOA) in the model to provide the fast routing recovery and reconstruct the network topology for path failure in H-WSNs. In the ICPSOA, mutation direction of the particle is determined by multi-swarm evolution equation, and its diversity is improved by immune mechanism, which can enhance the capacity of global search and improve the converging rate of the algorithm. Then we validate this theoretical model with simulation results. The results indicate that the ICPSOA-based fault-tolerant routing protocol outperforms several other protocols due to its capability of fast routing recovery mechanism, reliable communications, and prolonging the lifetime of WSNs.

A Swarm Optimization approach for clinical knowledge mining

“…M a n u s c r i p t 10 In addition to the discussed basic variations in the PSO algorithm, there are many more modified variants and applications of PSO found in literature. Some of them include Orthogonal Learning PSO [31], Gaussian-Distributed PSO [32], Comprehensive Learning PSO [33], Frankenstein's PSO [34], Cooperatively Coevolving PSO [35], Dissipative PSO [36], Distance-based Locally Informed PSO [37], Aging Leader and Challengers PSO [38], Crown-Jewel-Defence Strategy based PSO [39], Immune Cooperative PSO [40], Single solution PSO [41], Niching PSO [42], Chaos-PSO [43], Binary Multi-Objective PSO [44], PSO with Speciation and Adaptation [45], Discrete PSO [46] and Binary PSO [47].…”

Improved Barebones Particle Swarm Optimization with Neighborhood Search and Its Application on Ship Design

“…Step 3. For each particle , calculate its new position vector according to (3). Evaluate the fitness value of .…”

“…During the search process, each particle adjusts its search behavior according to the search experiences of its previous best position ( best ) and the global best position ( best ). Due to its simplicity and easy implementation, PSO has been successfully applied to various practical optimization problems [2][3][4][5].…”

Neighbor Constraint Assisted Distributed Localization for Wireless Sensor Networks

“…Wireless sensor networks (WSNs) composed of a large number of low-power sensors have been a subject of increased interest in recent years [1][2][3]. Location information of sensor nodes is vital for location-aware applications such as environmental monitoring, routing, and coverage control [4,5].…”