Universal strategy of dynamic subpopulation number management in practical network optimization problems

Abstract Multi-population approach, also known as Island Model (IM), is a well-known technique improving diversity preservation. It is employed by evolutionary and Swarm Intelligence methods. The diversity increased through the maintenance of many subpopulations, leads to finding higher quality solutions. Since IM is computationally expensive, it is important to keep the balance between the diversity maintenance and the convergence. Therefore, this paper concentrates on proposing the new strategy of Dynamic Subpopulation Number Control (SDSNC). When SDSNC is used, the number of subpopulations may change during the method run, adjusting the subpopulation number to the current method state, making such method highly-effective. In this paper, we show that the SDSNC strategies that were proposed for two up-to-date, NP-hard, practical problems are not universal. Therefore, we propose Store and Use Later (SUL) strategy that applied to problem-dedicated methods outperforms or at least equals two other strategies for both considered problems.

[1]  Petros Nicopolitidis,et al.  Energy-Efficient Lightpath Establishment in Backbone Optical Networks Based on Ant Colony Optimization , 2016, Journal of Lightwave Technology.

[2]  P. N. Suganthan,et al.  Multi-population differential evolution with balanced ensemble of mutation strategies for large-scale global optimization , 2015, Appl. Soft Comput..

[3]  Helton do Nascimento Alves.  A Multi-population Hybrid Algorithm to Solve Multi-objective Remote Switches Placement Problem in Distribution Networks , 2015 .

[4]  Luigi Fratta,et al.  The flow deviation method: An approach to store-and-forward communication network design , 1973, Networks.

[5]  Piero Castoldi,et al.  Trading Regeneration and Spectrum Utilization in Code-Rate Adaptive Flexi-Grid Networks , 2014, Journal of Lightwave Technology.

[6]  Bo Wang,et al.  Energy-efficient routing and bandwidth allocation in OFDM-based optical networks , 2016, IEEE/OSA Journal of Optical Communications and Networking.

[7]  Penousal Machado,et al.  Island models for cluster geometry optimization: how design options impact effectiveness and diversity , 2015, Journal of Global Optimization.

[8]  Mohamed Kurdi,et al.  An improved island model memetic algorithm with a new cooperation phase for multi-objective job shop scheduling problem , 2017, Comput. Ind. Eng..

[9]  Manuel Lozano,et al.  Artificial Bee Colony for optimization of cloud-ready and survivable elastic optical networks , 2018, Comput. Commun..

[10]  Róza Goscien.  Two metaheuristics for routing and spectrum allocation in cloud-ready survivable elastic optical networks , 2019, Swarm Evol. Comput..

[11]  Wei Lu,et al.  Efficient resource allocation for all-optical multicasting over spectrum-sliced elastic optical networks , 2013, IEEE/OSA Journal of Optical Communications and Networking.

[12]  Krzysztof Walkowiak,et al.  HEURISTIC ALGORITHMS FOR SURVIVABLE P2P MULTICASTING , 2013, Appl. Artif. Intell..

[13]  Michal Aibin,et al.  The evolutionary cost of Baldwin effect in the routing and spectrum allocation problem in elastic optical networks , 2017, Appl. Soft Comput..

[14]  Frédéric Saubion,et al.  A multi-population algorithm for multi-objective knapsack problem , 2018, Appl. Soft Comput..

[15]  Krzysztof Walkowiak,et al.  Towards solving practical problems of large solution space using a novel pattern searching hybrid evolutionary algorithm - An elastic optical network optimization case study , 2015, Expert Syst. Appl..

[16]  Kalyanmoy Deb,et al.  RapidAccurate Optimization of Difficult Problems Using Fast Messy Genetic Algorithms , 1993, ICGA.

[17]  Deep Medhi,et al.  Routing, flow, and capacity design in communication and computer networks , 2004 .

[18]  Halina Kwasnicka,et al.  Multi Population Pattern Searching Algorithm: A New Evolutionary Method Based on the Idea of Messy Genetic Algorithm , 2011, IEEE Transactions on Evolutionary Computation.

[19]  Reza Safabakhsh,et al.  Novel Adaptive Genetic Algorithm Sample Consensus , 2017, Appl. Soft Comput..

[20]  Goran Z. Markovic.  Routing and spectrum allocation in elastic optical networks using bee colony optimization , 2017, Photonic Network Communications.

[21]  Alexander Mendiburu,et al.  Distributed Estimation of Distribution Algorithms for continuous optimization: How does the exchanged information influence their behavior? , 2014, Inf. Sci..

[22]  Wanyi Gu,et al.  ACO-based routing and spectrum allocation in flexible bandwidth networks , 2013, Photonic Network Communications.

[23]  Mohamed Kurdi,et al.  An effective new island model genetic algorithm for job shop scheduling problem , 2016, Comput. Oper. Res..

[24]  Ioannis Tomkos,et al.  Elastic Bandwidth Allocation in Flexible OFDM-Based Optical Networks (vol 29, pg 1354, 2011) , 2011 .

[25]  Iwona Polak,et al.  Tabu Search in revealing the internal state of RC4+ cipher , 2019, Appl. Soft Comput..

[26]  Wayne D. Grover,et al.  Mesh-based Survivable Transport Networks: Options and Strategies for Optical, MPLS, SONET and ATM Networking , 2003 .

[27]  William F. Punch,et al.  Parameter-less population pyramid , 2014, GECCO.

[28]  Zbigniew Skolicki,et al.  Linkage in Island Models , 2008, Linkage in Evolutionary Computation.

[29]  Michal Przewozniczek,et al.  Active Multi-Population Pattern Searching Algorithm for flow optimization in computer networks - The novel coevolution schema combined with linkage learning , 2016, Inf. Sci..

[30]  Sidney L. Hantler,et al.  An Algorithm for Optimal Route Selection in SNA Networks , 1983, IEEE Trans. Commun..

[31]  Krzysztof Walkowiak,et al.  Tabu search algorithm for routing, modulation and spectrum allocation in elastic optical network with anycast and unicast traffic , 2015, Comput. Networks.

[32]  Krzysztof Walkowiak,et al.  Anycasting in connection-oriented computer networks: Models, algorithms and results , 2010, Int. J. Appl. Math. Comput. Sci..