An artificial bee colony algorithm for the capacitated vehicle routing problem

This paper introduces an artificial bee colony heuristic for solving the capacitated vehicle routing problem. The artificial bee colony heuristic is a swarm-based heuristic, which mimics the foraging behavior of a honey bee swarm. An enhanced version of the artificial bee colony heuristic is also proposed to improve the solution quality of the original version. The performance of the enhanced heuristic is evaluated on two sets of standard benchmark instances, and compared with the original artificial bee colony heuristic. The computational results show that the enhanced heuristic outperforms the original one, and can produce good solutions when compared with the existing heuristics. These results seem to indicate that the enhanced heuristic is an alternative to solve the capacitated vehicle routing problem. © 2011 Elsevier B.V. All rights reserved.postprin

Global gbest guided-artificial bee colony algorithm for numerical function optimization

Numerous computational algorithms are used to obtain a high performance in solving mathematics, engineering and statistical complexities. Recently, an attractive bio-inspired method—namely the Artificial Bee Colony (ABC)—has shown outstanding performance with some typical computational algorithms in different complex problems. The modification, hybridization and improvement strategies made ABC more attractive to science and engineering researchers. The two well-known honeybees-based upgraded algorithms, Gbest Guided Artificial Bee Colony (GGABC) and Global Artificial Bee Colony Search (GABCS), use the foraging behavior of the global best and guided best honeybees for solving complex optimization tasks. Here, the hybrid of the above GGABC and GABC methods is called the 3G-ABC algorithm for strong discovery and exploitation processes. The proposed and typical methods were implemented on the basis of maximum fitness values instead of maximum cycle numbers, which has provided an extra strength to the proposed and existing methods. The experimental results were tested with sets of fifteen numerical benchmark functions. The obtained results from the proposed approach are compared with the several existing approaches such as ABC, GABC and GGABC, result and found to be very profitable. Finally, obtained results are verified with some statistical testing

HYBRID ALGORITMA ARTIFICIAL BEE COLONY DAN ALGORITMA FIREFLY UNTUK MENYELESAIKAN DYNAMIC TRAVELLING SALESMAN PROBLEM (DTSP)

Penulisan skripsi ini bertujuan untuk menyelesaikan masalah Dynamic Travelling Salesman Problem (DTSP) menggunakan hybrid Algoritma Artificial Bee Colony (ABC) dan Algoritma Firefly. Dynamic Travelling Salesman Problem (DTSP) adalah suatu permasalahan pengoptimalan yang merupakan pengembangan dari Travelling Salesman Problem (TSP). DTSP memiliki dua tipe, yakni DTSP dengan penambahan atau pengurangan sejumlah kota tujuan, dan DTSP dengan jumlah kota tujuan tetap namun biaya antar kota tujuan dapat berubah. Metode yang digunakan untuk menyelesaikan permasalahan DTSP adalah hybrid Algoritma Artificial Bee Colony (ABC) dan Algoritma Firefly. Artificial Bee Colony (ABC) merupakan algoritma yang diinspirasi dari perilaku lebah dalam mencari makanan dan algoritma Firefly (FA) merupakan algoritma yang terinspirasi dari perilaku berkedipnya kunang-kunang. Hybrid Algoritma Artificial Bee Colony dan Algoritma Firefly adalah gabungan dari algoritma artificial bee colony dan algoritma firefly, dengan algoritma artificial bee colony sebagai proses pendahulu kemudian dilanjutkan dengan proses algoritma firefly. Penyelesaian DTSP menggunakan hybrid algoritma Artificial Bee Colony dan algoritma Firefly dibuat dalam bahasa pemrograman C++ serta diimplementasikan pada dua data yaitu data jarak berukuran kecil (15 kota) diperoleh 264 satuan jarak dan data jarak berukuran besar (128 kota) diperoleh 89851 satuan jarak. Berdasarkan hasil implementasi pada kedua kasus tersebut dapat disimpulkan bahwa semakin banyak jumlah iterasi dan jumlah koloni lebah atau firefly maka penyelesaiannya akan semakin baik, sedangkan untuk nilai alpha, semakin besar nilai alpha maka penyelesaiannya cenderung lebih baik

A hybrid of bacterial foraging and differential evolution -based distance of sequences

AbstractIn a previous work we presented a new distance that we called the sigma gram distance, which is used to compute the similarity between two sequences. This distance is based on parameters which we computed through an optimization process that used the artificial bee colony; a bio-inspired optimization algorithm. In this paper we show how a hybrid of two optimization algorithms; bacterial foraging and differential evolution, when used to compute the parameters of the sigma gram distance, can yield better results than those obtained by applying artificial bee colony. This superiority in performance is validated through experiments on the same data sets to which artificial bee colony, on the same optimization problem, was tested

An artificial bee colony algorithm for the capacitated vehicle routing problem

Session MF-03: Population-based metaheuristics for routing problems - Stream: Metaheuristics - Invited session no. 3This paper introduces an artificial bee colony heuristic for the capacitated vehicle routing problem. The artificial bee colony heuristic is a swarm-based heuristic, which mimics the foraging behavior of a honey bee swarm. The performance of the heuristic is evaluated on two sets of benchmark instances. A new scheme is also developed to improve the performance of the artificial bee colony heuristic. Computational results show that the heuristic with the new scheme produces good solutions.postprintThe 24th European Conference on Operational Research (EURO 24), Lisbon, Portual, 11-14 July 2010. In Abstract Book of EURO 24, 2010, p. 89, MF-03-

Quick Combinatorial Artificial Bee Colony -qCABC- Optimization Algorithm for TSP

Combinatorial Artificial Bee Colony Algorithm (CABC) is a new version of Artificial Bee Colony (ABC) to solve combinatorial type optimization problems and quick Artificial Bee Colony (qABC) algorithm is an improved version of ABC in which the onlooker bees behavior is modeled in more detailed way. Studies showed that qABC algorithm improves the convergence performance of standard ABC on numerical optimization. In this paper, to see the performance of this new modeling way of onlookers' behavior on combinatorial optimization, we apply the qABC idea to CABC and name this new algorithm as quick CABC (qCABC). qCABC is tested on Traveling Salesman Problem and simulation results show that qCABC algorithm improves the convergence and final performance of CABC

A modified scout bee for artificial bee colony algorithm and its performance on optimization problems

The artificial bee colony (ABC) is one of the swarm intelligence algorithms used to solve optimization problems which is inspired by the foraging behaviour of the honey bees. In this paper, artificial bee colony with the rate of change technique which models the behaviour of scout bee to improve the performance of the standard ABC in terms of exploration is introduced. The technique is called artificial bee colony rate of change (ABC-ROC) because the scout bee process depends on the rate of change on the performance graph, replace the parameter limit. The performance of ABC-ROC is analysed on a set of benchmark problems and also on the effect of the parameter colony size. Furthermore, the performance of ABC-ROC is compared with the state of the art algorithms

Training a Feed-forward Neural Network with Artificial Bee Colony Based Backpropagation Method

Back-propagation algorithm is one of the most widely used and popular techniques to optimize the feed forward neural network training. Nature inspired meta-heuristic algorithms also provide derivative-free solution to optimize complex problem. Artificial bee colony algorithm is a nature inspired meta-heuristic algorithm, mimicking the foraging or food source searching behaviour of bees in a bee colony and this algorithm is implemented in several applications for an improved optimized outcome. The proposed method in this paper includes an improved artificial bee colony algorithm based back-propagation neural network training method for fast and improved convergence rate of the hybrid neural network learning method. The result is analysed with the genetic algorithm based back-propagation method, and it is another hybridized procedure of its kind. Analysis is performed over standard data sets, reflecting the light of efficiency of proposed method in terms of convergence speed and rate.Comment: 14 Pages, 11 figure

PERANCANGAN PROGRAM APLIKASI OPTIMASI PENDISTRIBUSIAN BARANG MENGGUNAKAN ALGORITMA ARTIFICIAL BEE COLONY (ABC)

PERANCANGAN PROGRAM APLIKASI OPTIMASI PENDISTRIBUSIAN BARANG MENGGUNAKAN ALGORITMA ARTIFICIAL BEE COLONY (ABC)