Comparative Study of Different Memetic Algorithm Configurations for the Cyclic Bandwidth Sum Problem

The Cyclic Bandwidth Sum Problem (CBSP) is an NP-Hard Graph Embedding Problem which aims to embed a simple, finite graph (the guest) into a cycle graph of the same order (the host) while minimizing the sum of cyclic distances in the host between guest’s adjacent nodes. This paper presents preliminary results of our research on the design of a Memetic Algorithm (MA) able to solve the CBSP. A total of 24 MA versions, induced by all possible combinations of four selection schemes, two operators for recombination and three for mutation, were tested over a set of 25 representative graphs. Results compared with respect to the state-of-the-art top algorithm showed that all the tested MA versions were able to consistently improve its results and give us some insights on the suitability of the tested operators

Search Trajectory Networks Applied to the Cyclic Bandwidth Sum Problem

Search trajectory networks (STNs) were proposed as a tool to analyze the behavior of metaheuristics in relation to their exploration ability and the search space regions they traverse. The technique derives from the study of fitness landscapes using local optima networks (LONs). STNs are related to LONs in that both are built as graphs, modelling the transitions among solutions or group of solutions in the search space. The key difference is that STN nodes can represent solutions or groups of solutions that are not necessarily locally optimal. This work presents an STN-based study for a particular combinatorial optimization problem, the cyclic bandwidth sum minimization. STNs were employed to analyze the two leading algorithms for this problem: a memetic algorithm and a hyperheuristic memetic algorithm. We also propose a novel grouping method for STNs that can be generally applied to both continuous and combinatorial spaces

Energy efficiency in short and wide-area IoT technologies—A survey

In the last years, the Internet of Things (IoT) has emerged as a key application context in the design and evolution of technologies in the transition toward a 5G ecosystem. More and more IoT technologies have entered the market and represent important enablers in the deployment of networks of interconnected devices. As network and spatial device densities grow, energy efficiency and consumption are becoming an important aspect in analyzing the performance and suitability of different technologies. In this framework, this survey presents an extensive review of IoT technologies, including both Low-Power Short-Area Networks (LPSANs) and Low-Power Wide-Area Networks (LPWANs), from the perspective of energy efficiency and power consumption. Existing consumption models and energy efficiency mechanisms are categorized, analyzed and discussed, in order to highlight the main trends proposed in literature and standards toward achieving energy-efficient IoT networks. Current limitations and open challenges are also discussed, aiming at highlighting new possible research directions

An Ant Colony Optimisation Algorithm for Timetabling Problem

The University Course Timetabling Problem (UCTP) is a combinatorial optimization problem which involves the placement of events into timeslots and assignment of venues to these events. Different institutions have their peculiar problems; therefore there is a need to get an adequate knowledge of the problem especially in the area of constraints before applying an efficient method that will get a feasible solution in a reasonable amount of time. Several methods have been applied to solve this problem; they include evolutionary algorithms, tabu search, local search and swarm optimization methods like the Ant Colony Optimisation (ACO) algorithm. A variant of ACO called the MAX-MIN Ant System (MMAS) is implemented with two local search procedures (one main and one auxiliary) to tackle the UCTP using Covenant University problem instance. The local search design proposed was tailored to suit the problem tackled and was compared with other designs to emphasise the effect of neighbourhood combination pattern on the algorithm performance. From the experimental procedures, it was observed that the local search design proposed significantly bettered the existing one used for the comparison. The results obtained by the implemented algorithm proved that metaheuristics are highly effective when tackling real-world cases of the UCTP and not just generated instances of the problem and can even be better if some tangible modifications are made to it to perfectly suit a problem domain

High-Quality Hypergraph Partitioning

This dissertation focuses on computing high-quality solutions for the NP-hard balanced hypergraph partitioning problem: Given a hypergraph and an integer $k$, partition its vertex set into $k$ disjoint blocks of bounded size, while minimizing an objective function over the hyperedges. Here, we consider the two most commonly used objectives: the cut-net metric and the connectivity metric. Since the problem is computationally intractable, heuristics are used in practice - the most prominent being the three-phase multi-level paradigm: During coarsening, the hypergraph is successively contracted to obtain a hierarchy of smaller instances. After applying an initial partitioning algorithm to the smallest hypergraph, contraction is undone and, at each level, refinement algorithms try to improve the current solution. With this work, we give a brief overview of the field and present several algorithmic improvements to the multi-level paradigm. Instead of using a logarithmic number of levels like traditional algorithms, we present two coarsening algorithms that create a hierarchy of (nearly) $n$ levels, where $n$ is the number of vertices. This makes consecutive levels as similar as possible and provides many opportunities for refinement algorithms to improve the partition. This approach is made feasible in practice by tailoring all algorithms and data structures to the $n$-level paradigm, and developing lazy-evaluation techniques, caching mechanisms and early stopping criteria to speed up the partitioning process. Furthermore, we propose a sparsification algorithm based on locality-sensitive hashing that improves the running time for hypergraphs with large hyperedges, and show that incorporating global information about the community structure into the coarsening process improves quality. Moreover, we present a portfolio-based initial partitioning approach, and propose three refinement algorithms. Two are based on the Fiduccia-Mattheyses (FM) heuristic, but perform a highly localized search at each level. While one is designed for two-way partitioning, the other is the first FM-style algorithm that can be efficiently employed in the multi-level setting to directly improve $k$-way partitions. The third algorithm uses max-flow computations on pairs of blocks to refine $k$-way partitions. Finally, we present the first memetic multi-level hypergraph partitioning algorithm for an extensive exploration of the global solution space. All contributions are made available through our open-source framework KaHyPar. In a comprehensive experimental study, we compare KaHyPar with hMETIS, PaToH, Mondriaan, Zoltan-AlgD, and HYPE on a wide range of hypergraphs from several application areas. Our results indicate that KaHyPar, already without the memetic component, computes better solutions than all competing algorithms for both the cut-net and the connectivity metric, while being faster than Zoltan-AlgD and equally fast as hMETIS. Moreover, KaHyPar compares favorably with the current best graph partitioning system KaFFPa - both in terms of solution quality and running time

Journal of Telecommunications and Information Technology, 2018, nr 1

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Application of Computational Intelligence in Cognitive Radio Network for Efficient Spectrum Utilization, and Speech Therapy

communication systems utilize all the available frequency bands as efficiently as possible in time, frequency and spatial domains. Society requires more high capacity and broadband wireless connectivity, demanding greater access to spectrum. Most of the licensed spectrums are grossly underutilized while some spectrum (licensed and unlicensed) are overcrowded. The problem of spectrum scarcity and underutilization can be minimized by adopting a new paradigm of wireless communication scheme. Advanced Cognitive Radio (CR) network or Dynamic Adaptive Spectrum Sharing is one of the ways to optimize our wireless communications technologies for high data rates while maintaining users’ desired quality of service (QoS) requirements. Scanning a wideband spectrum to find spectrum holes to deliver to users an acceptable quality of service using algorithmic methods requires a lot of time and energy. Computational Intelligence (CI) techniques can be applied to these scenarios to predict the available spectrum holes, and the expected RF power in the channels. This will enable the CR to predictively avoid noisy channels among the idle channels, thus delivering optimum QoS at less radio resources. In this study, spectrum holes search using artificial neural network (ANN) and traditional search methods were simulated. The RF power traffic of some selected channels ranging from 50MHz to 2.5GHz were modelled using optimized ANN and support vector machine (SVM) regression models for prediction of real world RF power. The prediction accuracy and generalization was improved by combining different prediction models with a weighted output to form one model. The meta-parameters of the prediction models were evolved using population based differential evolution and swarm intelligence optimization algorithms. The success of CR network is largely dependent on the overall world knowledge of spectrum utilization in both time, frequency and spatial domains. To identify underutilized bands that can serve as potential candidate bands to be exploited by CRs, spectrum occupancy survey based on long time RF measurement using energy detector was conducted. Results show that the average spectrum utilization of the bands considered within the studied location is less than 30%. Though this research is focused on the application of CI with CR as the main target, the skills and knowledge acquired from the PhD research in CI was applied in ome neighbourhood areas related to the medical field. This includes the use of ANN and SVM for impaired speech segmentation which is the first phase of a research project that aims at developing an artificial speech therapist for speech impaired patients.Petroleum Technology Development Fund (PTDF) Scholarship Board, Nigeri

A Metaheuristic-Based Simulation Optimization Framework For Supply Chain Inventory Management Under Uncertainty

The need for inventory control models for practical real-world applications is growing with the global expansion of supply chains. The widely used traditional optimization procedures usually require an explicit mathematical model formulated based on some assumptions. The validity of such models and approaches for real world applications depend greatly upon whether the assumptions made match closely with the reality. The use of meta-heuristics, as opposed to a traditional method, does not require such assumptions and has allowed more realistic modeling of the inventory control system and its solution. In this dissertation, a metaheuristic-based simulation optimization framework is developed for supply chain inventory management under uncertainty. In the proposed framework, any effective metaheuristic can be employed to serve as the optimizer to intelligently search the solution space, using an appropriate simulation inventory model as the evaluation module. To be realistic and practical, the proposed framework supports inventory decision-making under supply-side and demand-side uncertainty in a supply chain. The supply-side uncertainty specifically considered includes quality imperfection. As far as demand-side uncertainty is concerned, the new framework does not make any assumption on demand distribution and can process any demand time series. This salient feature enables users to have the flexibility to evaluate data of practical relevance. In addition, other realistic factors, such as capacity constraints, limited shelf life of products and type-compatible substitutions are also considered and studied by the new framework. The proposed framework has been applied to single-vendor multi-buyer supply chains with the single vendor facing the direct impact of quality deviation and capacity constraint from its supplier and the buyers facing demand uncertainty. In addition, it has been extended to the supply chain inventory management of highly perishable products. Blood products with limited shelf life and ABO compatibility have been examined in detail. It is expected that the proposed framework can be easily adapted to different supply chain systems, including healthcare organizations. Computational results have shown that the proposed framework can effectively assess the impacts of different realistic factors on the performance of a supply chain from different angles, and to determine the optimal inventory policies accordingly

Advances in Evolutionary Algorithms

With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field