A Study Of Vantage Point Neighbourhood Search In The Bees Algorithm For Combinatorial Optimization Problems

Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2014Thesis (M.Sc. ) -- İstanbul Technical University, Institute of Science and Technology, 2014Bu tez çalışmasının temel amacı arıların kaynak arama davranışlarını modelleyen arı algoritmasının, kombinatoryal uzaylarda komşuluk arama fazına yeni bir yaklaşım geliştirilmesidir. Geliştirilen yaklaşım Gezgin Satıcı Problemine uygulanarak Gezgin Satıcı Problemi çözümünün en iyilenmesi amaçlanmıştır.This thesis focuses on nature-inspired optimisation algorithms, in particular, the Bees Algorithm that developed for combinatorial domains with new local search procedure and applied to Traveller Salesman Problem (TSP). An efficient and robust local neighborhood search algorithm is proposed for combinatorial domains to increase the efficiency of the Bees Algorithm.Yüksek LisansM.Sc

The design and applications of the african buffalo algorithm for general optimization problems

Optimization, basically, is the economics of science. It is concerned with the need to maximize profit and minimize cost in terms of time and resources needed to execute a given project in any field of human endeavor. There have been several scientific investigations in the past several decades on discovering effective and efficient algorithms to providing solutions to the optimization needs of mankind leading to the development of deterministic algorithms that provide exact solutions to optimization problems. In the past five decades, however, the attention of scientists has shifted from the deterministic algorithms to the stochastic ones since the latter have proven to be more robust and efficient, even though they do not guarantee exact solutions. Some of the successfully designed stochastic algorithms include Simulated Annealing, Genetic Algorithm, Ant Colony Optimization, Particle Swarm Optimization, Bee Colony Optimization, Artificial Bee Colony Optimization, Firefly Optimization etc. A critical look at these ‘efficient’ stochastic algorithms reveals the need for improvements in the areas of effectiveness, the number of several parameters used, premature convergence, ability to search diverse landscapes and complex implementation strategies. The African Buffalo Optimization (ABO), which is inspired by the herd management, communication and successful grazing cultures of the African buffalos, is designed to attempt solutions to the observed shortcomings of the existing stochastic optimization algorithms. Through several experimental procedures, the ABO was used to successfully solve benchmark optimization problems in mono-modal and multimodal, constrained and unconstrained, separable and non-separable search landscapes with competitive outcomes. Moreover, the ABO algorithm was applied to solve over 100 out of the 118 benchmark symmetric and all the asymmetric travelling salesman’s problems available in TSPLIB95. Based on the successful experimentation with the novel algorithm, it is safe to conclude that the ABO is a worthy contribution to the scientific literature

A Honey Bee Algorithm To Solve Quadratic Assignment Problem

Assigning facilities to locations is one of the important problems, which significantly is influence in transportation cost reduction. In this study, we solve quadratic assignment problem (QAP), using a meta-heuristic algorithm with deterministic tasks and equality in facilities and location number. It should be noted that any facility must be assign to only one location. In this paper, first of all, we have been described exact methods and heuristics, which are able to solve QAP; then we have been applied a meta-heuristic algorithm for it. QAP is a difficult problem and is in NP-hard class, so we have been used honey bee mating optimization (HBMO) algorithm to solve it.This method is new and have been applied and improved NP-hard problems. It’s a hybrid algorithm from Honey-Bee Mating system, simulated annealing and genetic algorithm.</p

Marriage in Honey Bees Optimization Algorithm for Flow-shop Problems

The objective of this work is to make a comparative study of the Marriage in Honeybees Op-timization (MBO) metaheuristic for flow-shop scheduling problems. This paper is focused on the design possibilities of the mating flight space shared by queens and drones. The proposed algorithm uses a 2-dimensional torus as an explicit mating space instead of the simulated an-nealing one in the original MBO. After testing different alternatives with benchmark datasets, the results show that the modeled and implemented metaheuristic is effective to solve flow-shop type problems, providing a new approach to solve other NP-Hard problems

An Overview of Evolutionary Algorithms toward Spacecraft Attitude Control

Evolutionary algorithms can be used to solve interesting problems for aeronautical and astronautical applications, and it is a must to review the fundamentals of the most common evolutionary algorithms being used for those applications. Genetic algorithms, particle swarm optimization, firefly algorithm, ant colony optimization, artificial bee colony optimization, and the cuckoo search algorithm are presented and discussed with an emphasis on astronautical applications. In summary, the genetic algorithm and its variants can be used for a large parameter space but is more efficient in global optimization using a smaller chromosome size such that the number of parameters being optimized simultaneously is less than 1000. It is found that PID controller parameters, nonlinear parameter identification, and trajectory optimization are applications ripe for the genetic algorithm. Ant colony optimization and artificial bee colony optimization are optimization routines more suited for combinatorics, such as with trajectory optimization, path planning, scheduling, and spacecraft load bearing. Particle swarm optimization, firefly algorithm, and cuckoo search algorithms are best suited for large parameter spaces due to the decrease in computation need and function calls when compared to the genetic algorithm family of optimizers. Key areas of investigation for these social evolution algorithms are in spacecraft trajectory planning and in parameter identification

Improving the bees algorithm for complex optimisation problems

An improved swarm-based optimisation algorithm from the Bees Algorithm family for solving complex optimisation problems is proposed. Like other Bees Algorithms, the algorithm performs a form of exploitative local search combined with random exploratory global search. This thesis details the development and optimisation of this algorithm and demonstrates its robustness. The development includes a new method of tuning the Bees Algorithm called Meta Bees Algorithm and the functionality of the proposed method is compared to the standard Bees Algorithm and to a range of state-of-the-art optimisation algorithms. A new fitness evaluation method has been developed to enable the Bees Algorithm to solve a stochastic optimisation problem. The new modified Bees Algorithm was tested on the optimisation of parameter values for the Ant Colony Optimisation algorithm when solving Travelling Salesman Problems. Finally, the Bees Algorithm has been adapted and employed to solve complex combinatorial problems. The algorithm has been combined with two neighbourhood operators to solve such problems. The performance of the proposed Bees Algorithm has been tested on a number of travelling salesman problems, including two problems on printed circuit board assembly machine sequencing

the Bees Algorithm: a novel optimisation tool

This work introduces the Bees Algorithm, a new optimisation algorithm inspired by the foraging behaviour of honey-bees. In its basic version, the Bees Algorithm performs a kind of neighbourhood search combined with global random search and can be used for both continuous and discrete optimisation problems. An improved version of the Bees Algorithm including replacing global random search with interpolation and extrapolation, shrinking neighbourhood size, and abandoning sites with no new information was developed. The improved version could solve benchmark function optimisation problems with less sampling of the search space. The Bees Algorithm has been applied to mechanical design optimisation. Two standard mechanical design problems, the design of a welded beam structure and the design of coil springs, were used to benchmark the Bees Algorithm against other optimisation techniques. Computer-aided preliminary design can be regarded as a special case of optimisation, where the goal is to generate as many solutions as possible above a predefined performance threshold. The higher the number of solutions satisfying the preliminary selection criteria, the greater is the chance to produce a good final solution. An adapted version of the Bees Algorithm for discrete function optimisation was developed and tested on a simple machine design task, preliminary gearbox design. The test consists of finding alternative gearbox configurations that approximately produce the required output speeds using one of the available input speeds. Experimental results show that the Bees Algorithm outperforms random search and a genetic optimisation algorithm. A modified version of the Bees Algorithm was used to search for multiple Pareto optimal solutions in a multi-objective optimisation design problem. Compared to two non-dominated genetic algorithms, the Bees Algorithm was able to find more trade-off solutions. Finally, the Bees Algorithm was employed to train Radial Basis Function (RBF) neural networks for two different problems. Despite the high dimensionality of the problems - each bee represented 2345 parameters in the control chart pattern recognition case and 1581 parameters in the wood defect classification case - the algorithm successfully trained very accurate classifiers. Although the accuracies achieved were marginally lower than those obtained with conventional RBF training methods, the total output errors were less than those for conventionally RBF-trained networks with same number of hidden neurons