Simulated evolution and simulated annealing algorithms for solving multi-objective open shortest path first weight setting problem

Optimal utilization of resources in present- day communication networks is a challenging task. Rout- ing plays an important role in achieving optimal re- source utilization. The open shortest path rst (OSPF) routing protocol is widely used for routing packets from a source node to a destination node. This protocol as- signs weights (or costs) to the links of a network. These weights are used to determine the shortest path be tween all sources to all destination nodes. Assignment of these weights to the links is classi ed as an NP-hard problem. This paper formulates the OSPF weight set- ting problem as a multi-objective optimization prob- lem, with maximum utilization, number of congested links, and number of unused links as the optimization objectives. Since the objectives are con icting in na- ture, an e cient approach is needed to balance the trade-o between these objectives. Fuzzy logic has been shown to e ciently solve multi-objective optimization problems. A fuzzy cost function for the OSPF weight setting problem is developed in this paper based on the Uni ed And-OR (UAO) operator. Two iterative heuris- tics, namely, simulated annealing (SA) and simulated evolution (SimE) have been implemented to solve the multi-objective OSPF weight setting problem using a fuzzy cost function. Results are compared with that found using other cost functions proposed in the literature [1]. Results suggest that, overall, the fuzzy cost function performs better than existing cost functions, with respect to both SA and SimE. Furthermore, SimE shows superior performance compared to SA. In addi- tion, a comparison of SimE with NSGA-II shows that, overall, SimE demonstrates slightly better performance in terms of quality of solutions.-http://link.springer.com/journal/10489hb201

A simulated annealing based genetic local search algorithm for multi-objective multicast routing problems

This paper presents a new hybrid evolutionary algorithm to solve multi-objective multicast routing problems in telecommunication networks. The algorithm combines simulated annealing based strategies and a genetic local search, aiming at a more flexible and effective exploration and exploitation in the search space of the complex problem to find more non-dominated solutions in the Pareto Front. Due to the complex structure of the multicast tree, crossover and mutation operators have been specifically devised concerning the features and constraints in the problem. A new adaptive mutation probability based on simulated annealing is proposed in the hybrid algorithm to adaptively adjust the mutation rate according to the fitness of the new solution against the average quality of the current population during the evolution procedure. Two simulated annealing based search direction tuning strategies are applied to improve the efficiency and effectiveness of the hybrid evolutionary algorithm. Simulations have been carried out on some benchmark multi-objective multicast routing instances and a large amount of random networks with five real world objectives including cost, delay, link utilisations, average delay and delay variation in telecommunication networks. Experimental results demonstrate that both the simulated annealing based strategies and the genetic local search within the proposed multi-objective algorithm, compared with other multi-objective evolutionary algorithms, can efficiently identify high quality non-dominated solution set for multi-objective multicast routing problems and outperform other conventional multi-objective evolutionary algorithms in the literature

The design of public transit networks with heuristic algorithms : case study Cape Town

Includes bibliographical references.The Transit Network Design Problem (TNDP) is well-researched in the field of transportation planning. It deals with the design of optimized public transportation networks and systems, and belongs to the class of non-linear optimization problems. In solving the problem, attempts are made to balance the tradeoffs between utility maximization and cost minimization given some resource constraints, within the context of a transportation network. In this dissertation, the design of a public transit network is undertaken and tested for Cape Town. The focus of the research is on obtaining an optimal network configuration that minimizes cost for both users and operators of the network. In doing so, heuristic solution algorithms are implemented in the design process, since they are known to generate better results for non-linear optimization problems than analytical ones. This algorithm which is named a Bus Route Network Design Algorithm (BRNDA) is based on genetic algorithms. Furthermore, it has three key components namely: 1) Bus Route Network Generation Algorithm (BRNGA) - which generates the potential network solutions; 2) Bus Route Network Analysis Procedure (BRNAP) - which evaluates the generated solutions; 3) Bus Route Network Search Algorithm (BRNSA) - which searches for an optimal or near optimal network option, among the feasible ones. The solution approach is tested first on a small scale network to demonstrate its numerical results, then it is applied to a large scale network, namely the Cape Town road network

Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

Combinatorial optimization and metaheuristics

Today, combinatorial optimization is one of the youngest and most active areas of discrete mathematics. It is a branch of optimization in applied mathematics and computer science, related to operational research, algorithm theory and computational complexity theory. It sits at the intersection of several fields, including artificial intelligence, mathematics and software engineering. Its increasing interest arises for the fact that a large number of scientific and industrial problems can be formulated as abstract combinatorial optimization problems, through graphs and/or (integer) linear programs. Some of these problems have polynomial-time (“efficient”) algorithms, while most of them are NP-hard, i.e. it is not proved that they can be solved in polynomial-time. Mainly, it means that it is not possible to guarantee that an exact solution to the problem can be found and one has to settle for an approximate solution with known performance guarantees. Indeed, the goal of approximate methods is to find “quickly” (reasonable run-times), with “high” probability, provable “good” solutions (low error from the real optimal solution). In the last 20 years, a new kind of algorithm commonly called metaheuristics have emerged in this class, which basically try to combine heuristics in high level frameworks aimed at efficiently and effectively exploring the search space. This report briefly outlines the components, concepts, advantages and disadvantages of different metaheuristic approaches from a conceptual point of view, in order to analyze their similarities and differences. The two very significant forces of intensification and diversification, that mainly determine the behavior of a metaheuristic, will be pointed out. The report concludes by exploring the importance of hybridization and integration methods

Fuzzy particle swarm optimization algorithms for the open shortest path first weight setting problem

The open shortest path first (OSPF) routing protocol is a well-known approach for routing packets from a source node to a destination node. The protocol assigns weights (or costs) to the links of a network. These weights are used to determine the shortest paths between all sources to all destination nodes. Assignment of these weights to the links is classified as an NP-hard problem. The aim behind the solution to the OSPF weight setting problem is to obtain optimized routing paths to enhance the utilization of the network. This paper formulates the above problem as a multi-objective optimization problem. The optimization metrics are maximum utilization, number of congested links, and number of unused links. These metrics are conflicting in nature, which motivates the use of fuzzy logic to be employed as a tool to aggregate these metrics into a scalar cost function. This scalar cost function is then optimized using a fuzzy particle swarm optimization (FPSO) algorithm developed in this paper. A modified variant of the proposed PSO, namely, fuzzy evolutionary PSO (FEPSO), is also developed. FEPSO incorporates the characteristics of the simulated evolution heuristic into FPSO. Experimentation is done using 12 test cases reported in literature. These test cases consist of 50 and 100 nodes, with the number of arcs ranging from 148 to 503. Empirical results have been obtained and analyzed for different values of FPSO parameters. Results also suggest that FEPSO outperformed FPSO in terms of quality of solution by achieving improvements between 7 and 31 %. Furthermore, comparison of FEPSO with various other algorithms such as Pareto-dominance PSO, weighted aggregation PSO, NSGA-II, simulated evolution, and simulated annealing algorithms revealed that FEPSO performed better than all of them by achieving best results for two or all three objectives.http://link.springer.com/journal/104892017-10-31hb2016Computer Scienc

QuASeR -- Quantum Accelerated De Novo DNA Sequence Reconstruction

In this article, we present QuASeR, a reference-free DNA sequence reconstruction implementation via de novo assembly on both gate-based and quantum annealing platforms. Each one of the four steps of the implementation (TSP, QUBO, Hamiltonians and QAOA) is explained with simple proof-of-concept examples to target both the genomics research community and quantum application developers in a self-contained manner. The details of the implementation are discussed for the various layers of the quantum full-stack accelerator design. We also highlight the limitations of current classical simulation and available quantum hardware systems. The implementation is open-source and can be found on https://github.com/prince-ph0en1x/QuASeR.Comment: 24 page