Bio-inspired Clustering Algorithm Research Articles

Bio-inspired multi-objective algorithms applied on production scheduling problems

Production scheduling is a crucial task in the manufacturing process. In this way, the managers must decide the job's production schedule. However, this task is not simple, often requiring complex software tools and specialized algorithms to find the optimal solution. In this work, a multi-objective optimization model was developed to explore production scheduling performance measures to help managers in decision-making related to job attribution under three simulations of parallel machine scenarios. Five important production scheduling performance measures were considered (makespan, tardiness and earliness times, number of tardy and early jobs), and combined into three objective functions. To solve the scheduling problem, three multi-objective evolutionary algorithms are considered (Multi-objective Particle Swarm Optimization, Multi-objective Grey Wolf Algorithm, and Non-dominated Sorting Genetic Algorithm II), and the set of optimum solutions named Pareto Front, provided by each one is compared in terms of dominance, generating a new Pareto Front, denoted as Final Pareto Front. Furthermore, this Final Pareto Front is analyzed through an automatic bio-inspired clustering algorithm based on the Genetic Algorithm. The results demonstrated that the proposed approach efficiently solves the scheduling problem considered. In addition, the proposed methodology provided more robust solutions by combining different bio-inspired multi-objective techniques. Furthermore, the cluster analysis proved fundamental for a better understanding of the results and support for choosing the final optimum solution.

Intelligent energy optimization for advanced IoT analytics edge computing on wireless sensor networks

The current dispensation of big data analytics requires innovative ways of data capturing and transmission. One of the innovative approaches is the use of a sensor device. However, the challenge with a sensor network is how to balance the energy load of wireless sensor networks, which can be achieved by selecting sensor nodes with an adequate amount of energy from a cluster. The clustering technique is one of the approaches to solve this challenge because it optimizes energy in order to increase the lifetime of the sensor network. In this article, a novel bio-inspired clustering algorithm was proposed for a heterogeneous energy environment. The proposed algorithm (referred to as DEEC-KSA) was integrated with a distributed energy-efficient clustering algorithm to ensure efficient energy optimization and was evaluated through simulation and compared with benchmarked clustering algorithms. During the simulation, the dynamic nature of the proposed DEEC-KSA was observed using different parameters, which were expressed in percentages as 0.1%, 4.5%, 11.3%, and 34% while the percentage of the parameter for comparative algorithms was 10%. The simulation result showed that the performance of DEEC-KSA is efficient among the comparative clustering algorithms for energy optimization in terms of stability period, network lifetime, and network throughput. In addition, the proposed DEEC-KSA has the optimal time (in seconds) to send a higher number of packets to the base station successfully. The advantage of the proposed bio-inspired technique is that it utilizes random encircling and half-life period to quickly adapt to different rounds of iteration and jumps out of any local optimum that might not lead to an ideal cluster formation and better network performance.

Hybrid Self-Organized Clustering Scheme for Drone Based Cognitive Internet of Things

Network management by using a cognitive approach is an attractive solution for drone-based Internet of Things (IoT) environment to provide many modern facilities to IoT users. In this paper, we try to minimize the networking related issues for drone-based IoT by providing a self-organized cluster-based networking solution. We propose a Hybrid Self-organized Clustering Scheme (HSCS) for drone-based cognitive IoT which utilizes a hybrid mechanism of glowworm swarm optimization (GSO) and dragonfly algorithm (DA). The proposed scheme contains cluster formation and cluster head selection mechanism based on GSO. Furthermore, we propose an effective cluster member tracking methodology using the behavioral study of DA which ensures efficient cluster management. The cluster maintenance is performed by a mechanism to identify dead cluster member which improves the stability of the network. Further routing mechanism is proposed for HSCS in which next hop neighbor for data transmission is selected by using the route selection function which ensures efficient communication. The performance of HSCS is evaluated in terms of cluster building time, energy consumption, cluster lifetime, and the probability of delivery success with existed hybrid bio-inspired clustering algorithm.

Una propuesta bioinspirada basada en vecindades para particionamiento

Una de las principales fuentes de inspiración para proponer nuevos paradigmas computacionales ha sido la observación de la naturaleza. Diversas técnicas en inteligencia artificial han surgido de esta manera. Uno de los esfuerzos que ha causado gran impacto es imitar la manera en que sobreviven otros seres vivos, y en particular, el estudio del fun- cionamiento cerebral es útil para proponer esquemas análogos y dar solución a algunos problemas. En este punto, los sistemas bioinspirados han surgido como un conjunto de modelos que están basados en el comportamiento y la forma de actuar de ciertos sistemas biológicos, los cuales pueden verse en áreas como la minería de datos e investigación de operaciones donde se distingue el agrupamiento de datos. A partir de la necesidad de resolver problemas de agrupamiento, hemos propuesto un algoritmo de particionamiento bioinspirado de búsqueda por vecindades. Este agrupamiento en una connotación bioinspirada, ha sido planteado después de observar algunas características comunes entre el particionamiento y la conducta del ser humano, donde dichas características pueden ser modeladas. Debido a la alta complejidad del particionamiento hemos incorporado la búsqueda por entorno variable (VNS) en el algoritmo de agrupamiento bioinspirado. La elección de esta metaheurística obedece a la semejanza que hay entre VNS y el modo en que los seres vivos se organizan para resolver situaciones de conflicto.

Comparisons among Bat algorithms with various objective functions on grouping photovoltaic power patterns

Photovoltaic power pattern clustering is fundamental in providing enhanced knowledge on the impacts of integrating photovoltaic systems into the electrical grid without extensive analysis and simulations. This paper investigates a set of clustering algorithms and validity indices to find the most efficient ones in grouping photovoltaic power patterns data. Furthermore, the introduction of the recently-developed bio-inspired optimization method, Bat, with various objective functions in clustering photovoltaic power patterns is presented. In order to evaluate the clustering results in a comprehensive manner, six internal validity indices are employed and a method to determine the optimum number of clusters is introduced. The clustering results on two datasets show that bio-inspired clustering algorithm Bat based on within-cluster-sum-of-squares to between-cluster variation (Bat WCBCR) as an objective function produces significantly high separated and well compact clusters.

Novel approaches for classification based on Cuckoo Search Strategy

This paper introduces a novel bio inspired clustering algorithm called Cuckoo Search Clustering Algorithm CSCA. This algorithm is based on the recently proposed Cuckoo Search Optimization technique which mimics the breeding strategy of the parasitic bird-cuckoo. The algorithm is further extended to a classification method, Biogeography Based Cuckoo Search Classification Algorithm BCSCA, which is a hybrid approach of the two nature inspired metaheuristic techniques. The proposed algorithms are validated with real time remote sensing satellite image datasets. The CSCA was first tested with benchmark dataset, which yields good results. Inspired by the results, it was applied on two real time remote sensing satellite image datasets for extraction of the water body, which itself is a quite complex problem. A new method for the generation of new cuckoos has been proposed, which is used in the algorithms. The resulting algorithm is conceptually simpler, takes less parameter than other nature inspired algorithms, and, after some parameter tuning, yields very good results. The extended algorithm BCSCA is also tested on the same satellite image for identifying different land covers by classifying the image in various classes. The algorithm was successful in classifying other land cover regions like rocky, barren, urban and vegetation. We strongly feel that results can be further improved by finer tuning of the parameters. Both the algorithms use Davies-Bouldin index DBI as fitness function. Further exploration of suggested algorithms CSCA and BCSCA may prove them to be strong entrants in the pool of nature inspired techniques.