Fuzzy Simulated Evolution Algorithm for Topology Design on Campus Networks

The topology design of campus networks is a hard contrained combinatorial optimization problem. It consists of deciding the number, type, and location of the active network elements (nodes) and links. This choice is dictated by physical and technological constraints and must optimize several objectives. Example of objectives are monetary cost, network delay, and hop count between communicating pairs. Furthermore, due to the nondeterministic nature of network traffic and other design parameters, the objectives criteria are imprecise. Fuzzy logic provides a suitable mathematical framework in such a situation. In this paper, we present an approach based on Simulated Evolution algorithm for the design of campus network topology. The two main phases of the algorithm, namely evaluation and allocation, have been fuzzified. To diversify the search, we have also incorporated Tabu Search-based characteristics in the allocation phase of the SE algorithm. This approach is then compared with Simulated Anealing algorithm, which is another well-known heuristic. Results show that on all test cases, Simulated Evolution algorithm more intelligent search of the solutions subspace and was able to find better solutions than Simulated Anealing

Redesign optimization for manufacturing using additive layer techniques

Improvements in additive manufacturing technologies have the potential to greatly provide value to designers that could also contribute towards improving the sustainability levels of products as well as the production of lightweight products. With these improvements, it is possible to eliminate the design restrictions previously faced by manufacturers. This study examines the principles of additive manufacturing, design guidelines, capabilities of the manufacturing processes and structural optimisation using topology optimisation. Furthermore, a redesign methodology is proposed and illustrated through a redesign case study of an existing bracket. The optimal design is selected using multi-criteria decision analysis method. The challenges for using additive manufacturing technologies are discussed

Traffic engineering in ambient networks: challenges and approaches

The focus of this paper is on traffic engineering in ambient networks. We describe and categorize different alternatives for making the routing more adaptive to the current traffic situation and discuss the challenges that ambient networks pose on traffic engineering methods. One of the main objectives of traffic engineering is to avoid congestion by controlling and optimising the routing function, or in short, to put the traffic where the capacity is. The main challenge for traffic engineering in ambient networks is to cope with the dynamics of both topology and traffic demands. Mechanisms are needed that can handle traffic load dynamics in scenarios with sudden changes in traffic demand and dynamically distribute traffic to benefit from available resources. Trade-offs between optimality, stability and signaling overhead that are important for traffic engineering methods in the fixed Internet becomes even more critical in a dynamic ambient environment

Simple Problems: The Simplicial Gluing Structure of Pareto Sets and Pareto Fronts

Quite a few studies on real-world applications of multi-objective optimization reported that their Pareto sets and Pareto fronts form a topological simplex. Such a class of problems was recently named the simple problems, and their Pareto set and Pareto front were observed to have a gluing structure similar to the faces of a simplex. This paper gives a theoretical justification for that observation by proving the gluing structure of the Pareto sets/fronts of subproblems of a simple problem. The simplicity of standard benchmark problems is studied.Comment: 10 pages, accepted at GECCO'17 as a poster paper (2 pages

Optimal Microgrid Topology Design and Siting of Distributed Generation Sources Using a Multi-Objective Substrate Layer Coral Reefs Optimization Algorithm

n this work, a problem of optimal placement of renewable generation and topology design for a Microgrid (MG) is tackled. The problem consists of determining the MG nodes where renewable energy generators must be optimally located and also the optimization of the MG topology design, i.e., deciding which nodes should be connected and deciding the lines’ optimal cross-sectional areas (CSA). For this purpose, a multi-objective optimization with two conflicting objectives has been used, utilizing the cost of the lines, C, higher as the lines’ CSA increases, and the MG energy losses, E, lower as the lines’ CSA increases. To characterize generators and loads connected to the nodes, on-site monitored annual energy generation and consumption profiles have been considered. Optimization has been carried out by using a novel multi-objective algorithm, the Multi-objective Substrate Layers Coral Reefs Optimization algorithm (Mo-SL-CRO). The performance of the proposed approach has been tested in a realistic simulation of a MG with 12 nodes, considering photovoltaic generators and micro-wind turbines as renewable energy generators, as well as the consumption loads from different commercial and industrial sites. We show that the proposed Mo-SL-CRO is able to solve the problem providing good solutions, better than other well-known multi-objective optimization techniques, such as NSGA-II or multi-objective Harmony Search algorithm.This research was partially funded by Ministerio de Economía, Industria y Competitividad, project number TIN2017-85887-C2-1-P and TIN2017-85887-C2-2-P, and by the Comunidad Autónoma de Madrid, project number S2013ICE-2933_02