Nature-inspired parameter controllers for ACO-based reactive search

This study proposes machine learning strategies to control the parameter adaptation in ant colony optimization algorithm, the prominent swarm intelligence metaheuristic.The sensitivity to parameters’ selection is one of the main limitations within the swarm intelligence algorithms when solving combinatorial problems.These parameters are often tuned manually by algorithm experts to a set that seems to work well for the problem under study, a standard set from the literature or using off-line parameter tuning procedures. In the present study, the parameter search process is integrated within the running of the ant colony optimization without incurring an undue computational overhead.The proposed strategies were based on a novel nature-inspired idea. The results for the travelling salesman and quadratic assignment problems revealed that the use of the augmented strategies generally performs well against other parameter adaptation methods

H-ACO: A Heterogeneous Ant Colony Optimisation approach with Application to the Travelling Salesman Problem

This is the author accepted manuscript. The final version is available from the publisher via the link in this record.Ant Colony Optimization (ACO) is a field of study that mimics the behaviour of ants to solve computationally hard problems. The majority of research in ACO focuses on homogeneous artificial ants although animal behaviour research suggests that heterogeneity of behaviour improves the overall efficiency of ant colonies. Therefore, this paper introduces and analyses the effects of heterogeneity of behavioural traits in ACO to solve hard optimisation problems. The developed approach implements different behaviour by introducing unique biases towards the pheromone trail and local heuristic (the next hop distance) for each ant. The well-known Ant System (AS) and Max-Min Ant System (MMAS) are used as the base algorithms to implement heterogeneity and experiments show that this method improves the performance when tested using several Travelling Salesman Problem (TSP) instances particularly for larger instances. The diversity preservation introduced by this algorithm helps balance exploration-exploitation, increases robustness with respect to parameter settings and reduces the number of algorithm parameters that need to be set.We would like to thank the Faculty of Electronics and Computer Engineering (FKEKK), Technical University of Malaysia Malacca (UTeM) and the Ministry of Higher Education (MoHE) Malaysia for the financial support under the SLAB/SlAI program

Controlling the Balance of Exploration and Exploitation in ACO Algorithm

خوارزمية النمل هي واحده من خوارزميات البحث عن الحلول المثلى ضمن فضاء واسع من الاحتمالات على نحو شبيه بطريقة النمل في البحث والتقفي لإيجاد الحلول لبعض المشاكل المعقدة التي يصعب حلها باستخدام خوارزميات الذكاء الاصطناعي التقليدية. تستخدم هذه الخوارزمية عمليه البحث في فضاء الحالات للاستنتاج حلول مختلفة اثناء عمليه البحث معتمدة على التوازن بين استكشاف حلول جديدة لتوسيع رقعة البحث وبين استغلال الحلول الجيدة لتحسين الحلول المستخرجة مسبقا. ان عمليه خلق توازن بين هاتين العمليتان يؤدي لتحسين النتائج والخروج بحلول أكثر امثليه.  هدف هذا البحث هو ايجاد قانون احتمالي أكثر ملاءمة وقادر على خلق توازن أفضل بين عمليتي الاستكشاف والاستغلال. بعد اجراء ستة تجارب مختلفة من حيث أشكال البينات تم اثبات ان التحسين في هذه الخوارزمية يؤدي الى انتاج حلول عالية الجودة من ناحية قصر طول المسار المكتشفAnt colony optimization is a meta-heuristic algorithm inspired by the foraging behavior of real ant colony. The algorithm is a population-based solution employed in different optimization problems such as classification, image processing, clustering, and so on. This paper sheds the light on the side of improving the results of traveling salesman problem produced by the algorithm. The key success that produces the valuable results is due to the two important components of exploration and exploitation. Balancing both components is the foundation of controlling search within the ACO. This paper proposes to modify the main probabilistic method to overcome the drawbacks of the exploration problem and produces global optimal results in high dimensional space. Experiments on six variant of ant colony optimization indicate that the proposed work produces high-quality results in terms of shortest route

Многопоточная маршрутизация в программно-конфигурируемых сетях

Unlike traditional networks, software-defined ones, which emerged not long ago, have not been completely investigated yet, including as it relates to the service quality concept implementation. The paper makes an attempt to implement the idea of multi-threaded traffic routing technology, used in traditional networks, in software-defined networking.Using a mathematical graph theory we have simulated the architecture of software-defined networking in the form of adjacency matrices of various dimensions. On the basis of comparative analysis we have formulated recommendations on the application of the Floyd–Warshall, Bellman–Ford–Moore and Dijkstra's algorithms to find the shortest paths, as well as the Ford–Fulkerson and Dinitz's algorithms for computing the maximum flow in a flow network in case of choosing network architectures of diverse complexity.We have considered different ways out of abnormal situations related to traffic dropouts caused by switch failures, network cable disconnection and other problems.Using a mathematical graph theory to describe the networks we have developed a multi-threaded routing algorithm in software-defined networks. The algorithm involved in the controller of software-defined networking allows:to increase the speed of data transmission and, consequently, a network bandwidth  through creating the several communication channels among the end-devices that is of particular relevance for high-load services;to implement route optimization in a network taking into account the requirements for individual network applications;to increase a uniformity of the network load;to reduce traffic downtime in case of channel break due to the rapid switch to an alternative route;to increase the delivery reliability of services sensitive to losses and delays by sending the correcting codes (for example, the Hamming code) or the replicated traffic on other routes.As a result of controlling the network flow parameters by controller, network applications start operating better, and end-users’ capabilities and efficiency becomes more.В отличие от традиционных сетей, программно-конфигурируемые сети появились недавно и не исследованы в полной мере, в том числе в части реализации концепции качества обслуживания. В данной работе сделана попытка реализовать идею технологии многопоточной маршрутизации трафика, применяемую в традиционных сетях, в программно-конфигурируемой сети.С использованием математической теории графов выполнено моделирование архитектуры программно-конфигурируемой сети в виде матриц смежности различной размерности. На основе проведенного сравнительного анализа сформулированы рекомендации по применению алгоритмов Флойда-Уоршелла,  Беллмана-Мура и Дейкстры для поиска кратчайшего пути, а также алгоритмов Форда-Фалкерсона и Диница для поиска максимального потока в случаях выбора сетевых архитектур различной сложности.Рассмотрены различные варианты выхода из нештатных ситуаций, связанных с пропаданием трафика из-за выхода из строя коммутаторов, отключения сетевых кабелей и прочих проблем.На основе применения математической теории графов для представления сетей разработан алгоритм многопоточной маршрутизации в программно-конфигурируемых сетях. Полученный алгоритм, включенный в контроллер программно-конфигурируемой сети, позволяет:повысить скорость передачи данных и, следовательно, пропускную способность сети за счет создания нескольких каналов связи между конечными устройствами, что особенно актуально для высоконагруженных сервисов; осуществить оптимизацию маршрутов в сети с учетом требований отдельных сетевых приложений;повысить равномерность загрузки сети;уменьшить время простоя трафика при обрыве какого-то из каналов благодаря быстрому переключению на альтернативный маршрут;увеличить надежность доставки чувствительных к потерям и задержкам сервисов путем отправки корректирующих кодов (например, код Хэмминга) или дублирующего трафика по другим маршрутам.  В результате оперативного управления контроллером параметрами потоков сети улучшается функционирование сетевых приложений, повышаются возможности и эффективность работы конечных пользователей

The Effect of Updating the Local Pheromone on ACS Performance using Fuzzy Logic

Fuzzy Logic Controller (FLC) has become one of the most frequently utilised algorithms to adapt the metaheuristics parameters as an artificial intelligence technique. In this paper, the parameter of Ant Colony System (ACS) algorithm is adapted by the use of FLC, and its behaviour is studied during this adaptation. The proposed approach is compared with the standard ACS algorithm. Computational results are done based on a library of sample instances for the Traveling Salesman Problem (TSPLIB)

NO-FIT-POLYGON/POLYHEDRON - ОРИЕНТИРОВАННАЯ АДАПТАЦИЯ МЕТОДА "МОДЕЛИРОВАНИЕ ОТЖИГА" ДЛЯ РЕШЕНИЯ ЗАДАЧИ НЕРЕГУЛЯРНОГО РАЗМЕЩЕНИЯ ГЕОМЕТРИЧЕСКИХ ОБЪЕКТОВ

В статье рассматривается задача нерегулярного размещения геометрических объектов (ГО). Для ее решения применяется метод "моделирование отжига" (Simulated Annealing - SA) и его модификации, адаптированные на базе применения No-Fit-Polygon/Polyhedron(NFP). Приводятся алгоритмы

Enabling swarm aggregation of position data via adaptive stigmergy: a case study in urban traffic flows

Urban road congestion estimation is a challenge in traffic management. City traffic state can vary temporally and spatially between road links, depending on crossroads and lanes. In addition, congestion estimation requires some sort of tuning to “what is around” to trigger appropriate reactions. An adaptive aggregation mechanism of position data is therefore crucial for traffic control. We present a biologically-inspired technique to aggregate position samples coming from on-vehicle devices. In essence, each vehicle position sample is spatially and temporally augmented with digital pheromone information, locally deposited and evaporated. As a consequence, an aggregated pheromone concentration appears and stays spontaneously while many stationary vehicles and high density roads occur. Pheromone concentration is then sharpened to achieve a better distinction of critical phenomena to be triggered as detected traffic events. The overall mechanism can be actually enabled if structural parameters are correctly tuned for the given application context. Determining such correct parameters is not a simple task since different urban areas have different traffic flux and density. Thus, an appropriate tuning to adapt parameters to the specific urban area is desirable to make the estimation effective. In this paper, we show how this objective can be achieved by using differential evolution

The behaviour of ACS-TSP algorithm when adapting both pheromone parameters using fuzzy logic controller

In this paper, an evolved ant colony system (ACS) is proposed by dynamically adapting the responsible parameters for the decay of the pheromone trails and using fuzzy logic controller (FLC) applied in the travelling salesman problems (TSP). The purpose of the proposed method is to understand the effect of both parameters and on the performance of the ACS at the level of solution quality and convergence speed towards the best solutions through studying the behavior of the ACS algorithm during this adaptation. The adaptive ACS is compared with the standard one. Computational results show that the adaptive ACS with dynamic adaptation of local pheromone parameter is more effective compared to the standard ACS

NO-FIT-POLYGON/POLYHEDRON - ОРИЕНТИРОВАННАЯ АДАПТАЦИЯ "МУРАВЬИНОГО АЛГОРИТМА" ДЛЯ РЕШЕНИЯ ЗАДАЧИ НЕРЕГУЛЯРНОГО РАЗМЕЩЕНИЯ ГЕОМЕТРИЧЕСКИХ ОБЪЕКТОВ

В статье рассматривается задача нерегулярного размещения геометрических объектов (ГО). Для ее решения применяется алгоритм "Муравьиной Колонии" (Ant Colonies - AC), адаптированный на базе применения No-Fit- Polygon/Polyhedron(NFP). Приводятся алгоритмы