Application of Max-min Ant System in Modelling the Inspectional Tour of Main Sales Points of Ghacem In Ghana.

Ant colony optimization (ACO) has widely been applied to solve combinatorial optimization problems in recent years. There are few studies, however, on its convergence time, which re?ects how many iteration times ACO algorithms spend in converging to the optimal solution. This study aims at using a Max-Min Ant System (MMAS), which belongs to Ants Algorithm to obtain optimal tour of the Travelling Salesman Problem of Ghacem. The study considered a twelve city node graph (major sales point of Ghacem) with the nodes representing the twelve cities, and the edges representing the major roads linking the cities. Secondary data of the inter-city driving distances was obtained from the Ghana Highway Authority. The results showed that the objective of finding the minimum tour from the Symmetric Travelling Salesman Problem (STSP) model by using Max-Min Ants System (MMAS) Algorithm was successfully achieved. The optimal route of total cost distance was found to be 1873Km. Therefore, Ghacem could minimize the cost of transportation  as the  Directors of Ghacem Ghana go on a tour to check on the sales performance  of the twelve key Distributors in the  major sales points in Ghana, starting from Tema where the company’s Head office is sited. It is very prudent for the company to rely on MMAS model to reduce fuel cost in order to maximize profit. In doing so it go along way to increase the tax revenue of the state. Keywords: Max-Min Ants System (MMAS), Ant Colony Optimization (ACO), Algorithm, Travelling Salesman (TSP), Ghace

OPTIMAL ROUTE DETERMINATION FOR POSTAL DELIVERY USING ANT COLONY OPTIMIZATION ALGORITHM

There are a lot of optimization challenges in the world, as we all know. The vehicle routing problem is one of the more complex and high-level problems. Vehicle Routing Problem is a real-life problem in the Postal Delivery System logistics and, if not properly attended to, can lead to wastage of resources that could have been directed towards other things. Several studies have been carried out to tackle this problem using different techniques and algorithms. This study used the Ant Colony Optimization Algorithm along with some powerful APIs to find an optimal route for the delivery of posts to customers in a Postal Delivering System. When Ant Colony Optimization Algorithm is used to solve the vehicle routing problem in transportation systems, each Ant's journey is mere “part” of a feasible solution. To put it in another way, numerous ants' pathways might make up a viable solution. Routes are determined for a delivery vehicle, with the objective of minimizing customer waiting time and operation cost. Experimental results indicate that the solution is optimal and more accurat

Discrete Flower Pollination Algorithm for solving the symmetric Traveling Salesman Problem

A dissertation submitted in fulfilment of the requirements for the degree of Masters of Science in Engineering (Electrical) to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, 2017The Travelling Salesman Problem (TSP) is an important NP-hard combinatorial optimisation problem that forms the foundation of many modern-day, practical problems such as logistics or network route planning. It is often used to benchmark discrete optimisation algorithms since it is a fundamental problem that has been widely researched. The Flower Pollination Algorithm (FPA) is a continuous optimisation algorithm that demonstrates promising results in comparison to other well-known algorithms. This research proposes the design, implementation and testing of two new algorithms based on the FPA for solving discrete optimisation problems, more specifically the TSP, namely the Discrete Flower Pollination Algorithm (DFPA) and the iterative Discrete Flower Pollination Algorithm (iDFPA). The iDFPA uses two proposed update methods, namely the Best Tour Update (BTU) and the Rejection Update (RU), to perform the iterative update process. The two algorithms are compared to the Ant Colony Optimisation’s (ACO) MAX−MIN Ant System (MMAS) as well as the Genetic Algorithm (GA) since they are well studied and developed. The DFPA and iDFPA results are significantly better than the GA and the iDFPA is able to outperform the ACO in all tested instances. The iDFPA with 300 iterations was able to achieve the optimal solution in the Berlin52 benchmark TSP problem as well as have improvements of up to 4.56% and 41.87% compared to the ACO and GA respectively. An analysis of how the RU and the annealing schedule used in the RU impacts on the overall results of the iDFPA is given. The RU analysis demonstrates how the annealing schedule can be manipulated to achieve certain results from the iDFPA such as faster convergence or better overall results. A parameter analysis is performed on both the DFPA and iDFPA for different TSP problem sizes and the suggested initial parameters for these algorithms are outlined.XL201

Multi Objectives Fuzzy Ant Colony Optimization Design of Supply Path Searching

One of problem faced in supply chain management is path searching. The best path depend not only on distance, but also other variables, such as: the quality of involved companies, quality of delivered product, and other value resulted by quality measurement. Commonly, the ant colony optimization could search the best path that has only one objective path. But it would be difficult to be adopted, because in the real case, the supply path has multi path and objectives (especially in palm oil based bioenergy supply). The objective of this paper is to improve the ant colony optimization for solving multi objectives based supply path problem by using fuzzy ant colony optimization. The developed multi objectives fuzzy ant colony optimization design was explained here, that it was used to search the best supply path. Salah satu masalah yang dihadapi dalam Supply Chain Management adalah pencarian jalur. Jalur terbaik tidak hanya tergantung pada jarak, tetapi juga variabel lain, seperti: kualitas Perusahaan yang terlibat, kualitas produk yang dikirimkan, dan nilai lain yang dipengaruhi oleh pengukuran kualitas. Umumnya, Ant Colony Optimization bisa mencari jalur terbaik yang hanya memiliki satu jalur objektif. Tapi akan sulit untuk diadopsi, karena dalam kasus nyata, jalur supply memiliki banyak jalur dan tujuan (khususnya pasokan minyak kelapa sawit berbasis bioenergi). Tujuan dari penelitian ini adalah untuk meningkatkan Ant Colony Optimization dalam menyelesaikan masalah jalur supply dengan menggunakan Fuzzy Ant Colony Optimization. Tujuan pengembangan Fuzzy Ant Colony Optimization dijelaskan disini, yaitu digunakan untuk mencari jalur supply terbaik

Stochastic Metaheuristics as Sampling Techniques using Swarm Intelligence

Optimization problems appear in many fields, as various as identification problems, supervised learning of neural networks, shortest path problems, etc. Metaheuristics [22] are a family of optimization algorithms, often applied to "hard " combinatorial problems for which no more efficient method is known. They have the advantage of being generi

A survey on metaheuristics for stochastic combinatorial optimization

Metaheuristics are general algorithmic frameworks, often nature-inspired, designed to solve complex optimization problems, and they are a growing research area since a few decades. In recent years, metaheuristics are emerging as successful alternatives to more classical approaches also for solving optimization problems that include in their mathematical formulation uncertain, stochastic, and dynamic information. In this paper metaheuristics such as Ant Colony Optimization, Evolutionary Computation, Simulated Annealing, Tabu Search and others are introduced, and their applications to the class of Stochastic Combinatorial Optimization Problems (SCOPs) is thoroughly reviewed. Issues common to all metaheuristics, open problems, and possible directions of research are proposed and discussed. In this survey, the reader familiar to metaheuristics finds also pointers to classical algorithmic approaches to optimization under uncertainty, and useful informations to start working on this problem domain, while the reader new to metaheuristics should find a good tutorial in those metaheuristics that are currently being applied to optimization under uncertainty, and motivations for interest in this fiel

Ant colony system-based applications to electrical distribution system optimization

Chapter 16, February 201

ACOustic: A nature-inspired exploration indicator for ant colony optimization

A statistical machine learning indicator, ACOustic, is proposed to evaluate the exploration behavior in the iterations of ant colony optimization algorithms. This idea is inspired by the behavior of some parasites in their mimicry to the queens’ acoustics of their ant hosts.The parasites’ reaction results from their ability to indicate the state of penetration.The proposed indicator solves the problem of robustness that results from the difference of magnitudes in the distance’s matrix, especially when combinatorial optimization problems with rugged fitness landscape are applied.The performance of the proposed indicator is evaluated against the existing indicators in six variants of ant colony optimization algorithms.Instances for travelling salesman problem and quadratic assignment problem are used in the experimental evaluation.The analytical results showed that the proposed indicator is more informative and more robust