A hybrid genetically-bacterial foraging algorithm converged by particle swarm optimisation for global optimisation

Jain, Tushar; Nigam, M. J.; Alavandar, Srinivasan

doi:10.1504/ijbic.2010.036160

Cited by 10 publications

(7 citation statements)

References 26 publications

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“…In this paper, we focus on swarm intelligence represented by the ant colony system and an artificial neural network simulating the brain's working principles and analyse the similarities between them by the approaches of complexity study, in order to reveal the inherent relationship among the different bio-inspired computation methods. The same analysis can also be obtained with other swarm intelligence, especially particle swarm optimization (Abraham et al, 2010;Deng et al, 2010;Jain et al, 2010;Lu et al, 2010;Singh et al, 2010). Firstly, the similarities between swarm intelligence and neural network have been analysed qualitatively from system structure and operation mechanisms.…”

Section: Discussionmentioning

confidence: 75%

An analytical approach to the similarities between swarm intelligence and artificial neural network

Xiao

Tao

Chen

2011

Transactions of the Institute of Measurement and Control

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In this paper, the authors focus on swarm intelligence represented by the ant colony system and artificial neural network simulating the brain working principles, and analyse the similarities between them by the approaches of a complexity study. Firstly, the similarities between swarm intelligence and neural network have been analysed qualitatively from system structure and operation mechanisms and the results show that there exist obvious similarities in overall disciplines between them. Moreover, the mapping relationships between swarm intelligence and artificial neural network at algorithm level have been revealed using the travelling salesman problem (TSP), which illustrates that these two algorithms are also similar from the viewpoint of numeric computing and simulating when they are directly used to solve a complex problem. In addition, the swarm intelligence algorithm and the artificial neural network algorithm are adopted to solve an engineering practical problem, which can be transformed into the TSP. The results show that the performance of these two algorithms still has close similarities. The purpose of the above work is to construct the inherent relationship among different bio-inspired computation methods, which has both important theoretic significance and practical value to reveal the generation and operation mechanisms of human intelligence.

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Section: Discussionmentioning

confidence: 75%

An analytical approach to the similarities between swarm intelligence and artificial neural network

Xiao

Tao

Chen

2011

Transactions of the Institute of Measurement and Control

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“…Finally, the result is decoded to get the individual with best optimum value. PSO which simulates the social behaviour of the movement of organisms like bird flock or fish school has been used extensively for various optimising problems (Kennedy and Eberhart, 1995;Jain et al, 2010;Miao et al, 2009;Das et al, 2008). Various approaches based on PSO in scheduling can be seen in (Rodriguez and Buyya, 2014;Pandey et al, 2010;Wu et al, 2010).…”

Section: Related Workmentioning

confidence: 99%

A Bacterial Foraging based Batch Scheduling Model for Distributed System

Alam

Raza

2016

IJBIC

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The problem of scheduling in the parallel and distributed environment is proven to be NP-complete and has been addressed by various heuristics. It is always desired from a scheduling scheme to distribute the load evenly on the available resources so as to have maximum resource utilisation while meeting the scheduling objective(s). Bio-inspired metaheuristics for job scheduling have gained immense popularity due to their effectiveness in providing near optimal solution in a reasonable time and computational complexity. This work proposes an evolutionary static scheduling technique based on bacterial foraging for a batch of independent jobs. This model generates the schedule minimising the node idle time and the makespan while exhibiting a balanced load distribution with minimum run time overhead. Simulation study proves the effectiveness of the proposed model in comparison with its peers.

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“…Yang et al (2007) denoted that PSO is rapidly converging towards an optimum, simple to compute, easy to implement and free from the complex computation in GA. Also, Zhang et al (2006) and Zhang and Cai (2010) applied both PSO and GA on limited resource programming, and the results suggested that the performance of PSO is better than that of GA. There are still many other variants of PSO (Abraham et al, 2010;Deng et al, 2010;Jain et al, 2010;Lu et al, 2010;Singh et al, 2010); for more details please refer to the corresponding references. Since the operating efficiency of PSO is better than that of GA, the proposed solving approach TPSO, in this study, is based on PSO.…”

Section: The Concept Of Particle Swarm Optimizationmentioning

confidence: 99%

Using analytic network process and turbo particle swarm optimization algorithm for non-balanced supply chain planning considering supplier relationship management

Che

Chiang

Che

2011

Transactions of the Institute of Measurement and Control

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Supply chain planning has been regarded as a key strategic decision-making activity for the enterprises under the current business circumstances. From the point of supply chain planning, the important issues are to find suitable and quality partners and to decide upon an appropriate productiondistribution plan. In this study, hence, we address to develop a decision methodology for supply chain planning in multi-echelon non-balanced supply chain system, taking into account such four criteria as cost, quality, delivery and supplier relationship management and considering quantity discount and capacity constraints. The proposed methodology is based on the analytic network process and turbo particle swarm optimization (TPSO), to evaluate partners and to determine an optimal supply chain network pattern and production-distribution mode. Finally, to demonstrate the performance of the proposed TPSO algorithm, comparative numerical experiments are performed by TPSO, particle swarm optimization (PSO) and genetic algorithm (GA). Empirical analysis results demonstrate that TPSO can outperform PSO and GA in non-balanced supply chain planning problems.

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A hybrid genetically-bacterial foraging algorithm converged by particle swarm optimisation for global optimisation

Cited by 10 publications

References 26 publications

An analytical approach to the similarities between swarm intelligence and artificial neural network

An analytical approach to the similarities between swarm intelligence and artificial neural network

A Bacterial Foraging based Batch Scheduling Model for Distributed System

Using analytic network process and turbo particle swarm optimization algorithm for non-balanced supply chain planning considering supplier relationship management

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