scholarly journals Efficient Computation of Shortest Paths in Networks Using Particle Swarm Optimization and Noising Metaheuristics

2007 ◽  
Vol 2007 ◽  
pp. 1-25 ◽  
Author(s):  
Ammar W. Mohemmed ◽  
Nirod Chandra Sahoo
Keyword(s):  
Particle Swarm Optimization ◽  
Local Search ◽  
Shortest Path ◽  
Hybrid Algorithm ◽  
Shortest Paths ◽  
Particle Swarm ◽  
Elementary Transformation ◽  
Efficient Computation ◽  
Hybrid Search ◽  
Swarm Optimization

This paper presents a novel hybrid algorithm based on particle swarm optimization (PSO) and noising metaheuristics for solving the single-source shortest-path problem (SPP) commonly encountered in graph theory. This hybrid search process combines PSO for iteratively finding a population of better solutions and noising method for diversifying the search scheme to solve this problem. A new encoding/decoding scheme based on heuristics has been devised for representing the SPP parameters as a particle in PSO. Noising-method-based metaheuristics (noisy local search) have been incorporated in order to enhance the overall search efficiency. In particular, an iteration of the proposed hybrid algorithm consists of a standard PSO iteration and few trials of noising scheme applied to each better/improved particle for local search, where the neighborhood of each such particle is noisily explored with an elementary transformation of the particle so as to escape possible local minima and to diversify the search. Simulation results on several networks with random topologies are used to illustrate the efficiency of the proposed hybrid algorithm for shortest-path computation. The proposed algorithm can be used as a platform for solving other NP-hard SPPs.

scholarly journals Energy Efficient Particle Swarm Optimization Based Multipath Routing in WSN

2018 ◽  
Vol 4 (10) ◽  
pp. 5
Author(s):  
Roshni Jha ◽  
Dr. Shivnath Ghosh
Keyword(s):  
Particle Swarm Optimization ◽  
Shortest Path ◽  
Routing Protocol ◽  
Energy Efficient ◽  
Energy Requirement ◽  
Shortest Paths ◽  
Multipath Routing ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Shortest Path Routing

Wireless Networks includes a larger advantage in today’s communication application like environmental, traffic, military, and health observation. To realize these applications it's necessary to possess a reliable routing protocol. discusses about the working of proposed energy efficient bandwidth aware shortest path routing protocol for multipath routing in wireless sensor network. The proposed algorithm is based for choosing energy efficient shortest path. In routing algorithm, route that have shortest path among multipaths selected by particle swarm optimization algorithm. Among these shortest paths, that path is selected which require minimum route selection parameter. The proposed algorithm uses distance as well as energy of nodes as a parameter to find optimum paths using particle swarm optimization. Among these selected paths, only one optimum path is selected which reduces the energy requirement of the network. According to this work there would be improvement in other parameters also such as end to end delay as well as throughput.

scholarly journals Memes Evolution in a Memetic Variant of Particle Swarm Optimization

Mathematics ◽  
2019 ◽  
Vol 7 (5) ◽  
pp. 423 ◽  
Author(s):  
Umberto Bartoccini ◽  
Arturo Carpi ◽  
Valentina Poggioni ◽  
Valentino Santucci
Keyword(s):  
Particle Swarm Optimization ◽  
Local Search ◽  
Particle Swarm ◽  
Search Space ◽  
Benchmark Problems ◽  
Hybrid Search ◽  
Swarm Optimization ◽  
Search Spaces ◽  
Starting Point ◽  
Adaptive Pso

In this work, a coevolving memetic particle swarm optimization (CoMPSO) algorithm is presented. CoMPSO introduces the memetic evolution of local search operators in particle swarm optimization (PSO) continuous/discrete hybrid search spaces. The proposed solution allows one to overcome the rigidity of uniform local search strategies when applied to PSO. The key contribution is that memes provides each particle of a PSO scheme with the ability to adapt its exploration dynamics to the local characteristics of the search space landscape. The objective is obtained by an original hybrid continuous/discrete meme representation and a probabilistic co-evolving PSO scheme for discrete, continuous, or hybrid spaces. The coevolving memetic PSO evolves both the solutions and their associated memes, i.e. the local search operators. The proposed CoMPSO approach has been experimented on a standard suite of numerical optimization benchmark problems. Preliminary experimental results show that CoMPSO is competitive with respect to standard PSO and other memetic PSO schemes in literature, and its a promising starting point for further research in adaptive PSO local search operators.

scholarly journals An Enhanced Multi-Objective Particle Swarm Optimization in Water Distribution Systems Design

Water ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1334
Author(s):  
Mohamed R. Torkomany ◽  
Hassan Shokry Hassan ◽  
Amin Shoukry ◽  
Ahmed M. Abdelrazek ◽  
Mohamed Elkholy
Keyword(s):  
Particle Swarm Optimization ◽  
Local Search ◽  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution Systems ◽  
Particle Swarm ◽  
Large Network ◽  
Swarm Optimization ◽  
Original Algorithm ◽  
Multi Objective

The scarcity of water resources nowadays lays stress on researchers to develop strategies aiming at making the best benefit of the currently available resources. One of these strategies is ensuring that reliable and near-optimum designs of water distribution systems (WDSs) are achieved. Designing WDSs is a discrete combinatorial NP-hard optimization problem, and its complexity increases when more objectives are added. Among the many existing evolutionary algorithms, a new hybrid fast-convergent multi-objective particle swarm optimization (MOPSO) algorithm is developed to increase the convergence and diversity rates of the resulted non-dominated solutions in terms of network capital cost and reliability using a minimized computational budget. Several strategies are introduced to the developed algorithm, which are self-adaptive PSO parameters, regeneration-on-collision, adaptive population size, and using hypervolume quality for selecting repository members. A local search method is also coupled to both the original MOPSO algorithm and the newly developed one. Both algorithms are applied to medium and large benchmark problems. The results of the new algorithm coupled with the local search are superior to that of the original algorithm in terms of different performance metrics in the medium-sized network. In contrast, the new algorithm without the local search performed better in the large network.

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