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 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

Decision of Multimodal Transportation Scheme Based on Swarm Intelligence

In this paper, some basic concepts of multimodal transportation and swarm intelligence were described and reviewed and analyzed related literatures of multimodal transportation scheme decision and swarm intelligence methods application areas. Then, this paper established a multimodal transportation scheme decision optimization mathematical model based on transportation costs, transportation time, and transportation risks, explained relevant parameters and the constraints of the model in detail, and used the weight coefficient to transform the multiobjective optimization problems into a single objective optimization transportation scheme decision problem. Then, this paper is proposed by combining particle swarm optimization algorithm and ant colony algorithm (PSACO) to solve the combinatorial optimization problem of multimodal transportation scheme decision for the first time; this algorithm effectively combines the advantages of particle swarm optimization algorithm and ant colony algorithm. The solution shows that the PSACO algorithm has two algorithms’ advantages and makes up their own problems; PSACO algorithm is better than ant colony algorithm in time efficiency and its accuracy is better than that of the particle swarm optimization algorithm, which is proved to be an effective heuristic algorithm to solve the problem about multimodal transportation scheme decision, and it can provide economical, reasonable, and safe transportation plan reference for the transportation decision makers

Discrete particle swarm optimization for combinatorial problems with innovative applications.

Master of Science in Computer Science. University of KwaZulu-Natal, Durban 2016.Abstract available in PDF file

Computational Numerical Solution for Traveling Salesman Problem

This paper examined and analyzed the desire of Traveling Salesman Problem (TSP) to find the cheapest way of visiting all given set of cities and returning to the starting point.     We presented a unique decomposition approach model for TSP in which the requirements and features of practical application in communication network, road transportation and supply chains are put into consideration.  We used a Mathematical Modeling solution with the application of Ant Colony Search Algorithm (ACSA) approach for result computation.  In our approach, different Agents were created for difference purposes.   Information agent gathered information about best tour and detected the solution agent that arrived at a given point with information message containing details of where the solution agent has come from as well as best tour cost.  The place ant performs local pheromone decay on the relevant links.   This help to avoid random visit to irrelevant edges and allows the place ant to calculate the cost of tour of all place ants including the latest pheromone level on the links to each of the place ants. The solution agent uses available information to decide  which node to visit next and informs the place ant of  its decision to move to a given destination and update better tour  previously sampled while information about where to go next also obtained.  The place ant updates its pheromone value for that link using the equivalent of the algorithm for local pheromone update.  The cycle continues until solution agent arrives at its destination. The main advantage of our approach is that it permits the use of mixed integer programming and combinatorial optimization techniques to compute real optimal routing path, solving the problem in practice by returning actual shortest route with its numerical value and not the best effort result as provided by some previous models and analytical methods. The implementation was carried out using C# programming language.  Data used were generated and the performance evaluation of the model was carried out through simulation using Matlab 7.0.  The result shows that by considering all possible paths between a node as the source and another as the destination, all possible routes for a particular journey with shortest route in each case were generated. Keywords: Ant Colony, Combinatorial Optimization, Mixed Integer Programming, Pheromone, Search Algorithm and Traveling Salesman

Combinatorial Ant Optimization and the Flowshop Problem

Researchers have developed efficient techniques, meta-heuristics to solve many Combinatorial Optimization (CO) problems, e.g., Flow shop Scheduling Problem, Travelling Salesman Problem (TSP) since the early 60s of the last century. Ant Colony Optimization (ACO) and its variants were introduced by Dorigo et al. [DBS06] in the early 1990s which is a technique to solve CO problems. In this thesis, we used the ACO technique to find solutions to the classic Flow shop Scheduling Problem and proposed a novel method for solution improvement. Our solution is composed of two phases; in the first phase, we solved TSP using ACO technique which gave us an initial permutation or tour. We used the same trip as an initial solution for our problem and then improved it by using 2-opt exchanges which yielded in a promising result. Furthermore, we introduced another improvement technique which gave us a more promising result. We have compared our results with the best (optimal) and worst solution known till date. A comprehensive experimental study using existing dataset proves that our approach remarkably gives good results