Distributed approaches for reference-point-based multi-objective hybrid problems

Abstract Testing and performance comparisons for optimization algorithms and methods are an important part of demonstrating accurate behavior. These tests are accomplished using numerical and graphical illustrations of the results obtained from the proposed algorithms. To emphasize the advantages and disadvantages of the proposed approaches and algorithms, a set of problems for which the solution and common properties are known is needed. Thus, the behavior of the algorithm as it obtains the solution set can be explained by using the common properties of the test problems. Therefore, a set of well-known benchmark problems has been proposed by researchers, and a portion of these problems is specifically designed for testing multi-objective optimization algorithms. Although these problems are sufficient to present the performances of optimization algorithms, there is no problem set for investigating the distributed performance of optimization algorithms. Hence, a method for the performance comparison of distribution methods for multi-objective optimization algorithms is needed. In this study, a set of new test problems, called hybrid problems, is defined by aligning two different well-known test functions for parallelization models. These novel problems are solved using the distributed models. Lastly, a set of approaches is proposed to increase the performance of any similar distributed models.

[1]  Carlos A. Coello Coello,et al.  Applications of Parallel Platforms and Models in Evolutionary Multi-Objective Optimization , 2009 .

[2]  Kalyanmoy Deb,et al.  Reference point based distributed computing for multiobjective optimization , 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).

[3]  Lhassane Idoumghar,et al.  Comparison of Two Diversification Methods to Solve the Quadratic Assignment Problem , 2015, ICCS.

[4]  Xia Li,et al.  A new hybrid memetic multi-objective optimization algorithm for multi-objective optimization , 2018, Inf. Sci..

[5]  Jorge González-Domínguez,et al.  Accelerating binary biclustering on platforms with CUDA-enabled GPUs , 2019, Inf. Sci..

[6]  Marta S. R. Monteiro,et al.  The hop-constrained minimum cost flow spanning tree problem with nonlinear costs: an ant colony optimization approach , 2015, Optim. Lett..

[7]  Carlos A. Coello Coello,et al.  Hybridizing surrogate techniques, rough sets and evolutionary algorithms to efficiently solve multi-objective optimization problems , 2008, GECCO '08.

[8]  Kalyanmoy Deb,et al.  Distributed Computing of Pareto-Optimal Solutions with Evolutionary Algorithms , 2003, EMO.

[9]  M. A. El-Shorbagy,et al.  Local search based hybrid particle swarm optimization algorithm for multiobjective optimization , 2012, Swarm Evol. Comput..

[10]  Qiuzhen Lin,et al.  A novel hybrid multi-objective immune algorithm with adaptive differential evolution , 2015, Comput. Oper. Res..

[11]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[12]  Xianpeng Wang,et al.  Multi-objective optimization using a hybrid differential evolution algorithm , 2012, 2012 IEEE Congress on Evolutionary Computation.

[13]  Antoni Wibowo,et al.  An effective model of multiple multi-objective evolutionary algorithms with the assistance of regional multi-objective evolutionary algorithms: VIPMOEAs , 2013, Appl. Soft Comput..

[14]  Chuan Wang,et al.  Self-adapting hybrid strategy particle swarm optimization algorithm , 2016, Soft Comput..

[15]  C. Leon,et al.  Optimizing the Configuration of a Broadcast Protocol through Parallel Cooperation of Multi-objective Evolutionary Algorithms , 2008, 2008 The Second International Conference on Advanced Engineering Computing and Applications in Sciences.

[16]  Ferrante Neri,et al.  Covariance matrix adaptation pareto archived evolution strategy with hypervolume-sorted adaptive grid algorithm , 2016, Integr. Comput. Aided Eng..

[17]  El-Ghazali Talbi,et al.  Parallel cooperative meta-heuristics on the computational grid.: A case study: the bi-objective Flow-Shop problem , 2006, Parallel Comput..

[18]  Darrell Whitley,et al.  NK Hybrid Genetic Algorithm for Clustering , 2018, IEEE Transactions on Evolutionary Computation.

[19]  Ponnuthurai N. Suganthan,et al.  An improved differential evolution algorithm using efficient adapted surrogate model for numerical optimization , 2018, Inf. Sci..

[20]  Sriparna Saha,et al.  Reference point based archived many objective simulated annealing , 2018, Inf. Sci..

[21]  Ferrante Neri,et al.  A fast hypervolume driven selection mechanism for many-objective optimisation problems , 2017, Swarm Evol. Comput..

[22]  Walter D. Potter,et al.  Estimation of distribution algorithm enhanced particle swarm optimization for water distribution network optimization , 2015, Frontiers of Environmental Science & Engineering.

[23]  Djamel Djenouri,et al.  Exploiting GPU parallelism in improving bees swarm optimization for mining big transactional databases , 2019, Inf. Sci..

[24]  Joshua D. Knowles,et al.  ParEGO: a hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems , 2006, IEEE Transactions on Evolutionary Computation.

[25]  Antonio Costa,et al.  Minimizing the total completion time on a parallel machine system with tool changes , 2016, Comput. Ind. Eng..

[26]  Andrew Lewis,et al.  Parallel multi-objective optimization using Master-Slave model on heterogeneous resources , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[27]  Xiao-Liang Shen,et al.  A hybrid particle swarm optimization algorithm using adaptive learning strategy , 2018, Inf. Sci..

[28]  Michael G. Epitropakis,et al.  Progressive preference articulation for decision making in multi-objective optimisation problems , 2017, Integr. Comput. Aided Eng..

[29]  Mohamed Kurdi,et al.  A new hybrid island model genetic algorithm for job shop scheduling problem , 2015, Comput. Ind. Eng..

[30]  Kusum Deep,et al.  A Hybrid Harmony search and Simulated Annealing algorithm for continuous optimization , 2018, Inf. Sci..

[31]  Xianpeng Wang,et al.  A Hybrid Multiobjective Evolutionary Algorithm for Multiobjective Optimization Problems , 2013, IEEE Transactions on Evolutionary Computation.

[32]  Vincent Roberge,et al.  Collaborative Parallel Hybrid Metaheuristics on Graphics Processing Unit , 2015, Int. J. Comput. Intell. Appl..

[33]  Jun Zhang,et al.  Distributed Cooperative Co-Evolution With Adaptive Computing Resource Allocation for Large Scale Optimization , 2019, IEEE Transactions on Evolutionary Computation.

[34]  Umut Tosun,et al.  On the performance of parallel hybrid algorithms for the solution of the quadratic assignment problem , 2015, Eng. Appl. Artif. Intell..

[35]  Kalyanmoy Deb,et al.  Evaluation of the migrated solutions for distributing reference point-based multi-objective optimization algorithms , 2018, Inf. Sci..

[36]  Yousef Naranjani,et al.  A genetic algorithm and cell mapping hybrid method for multi-objective optimization problems , 2014, 2014 11th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE).

[37]  Zhang Yi,et al.  IGD Indicator-Based Evolutionary Algorithm for Many-Objective Optimization Problems , 2018, IEEE Transactions on Evolutionary Computation.

[38]  Min Liu,et al.  Novel prediction and memory strategies for dynamic multiobjective optimization , 2015, Soft Comput..

[39]  Kedar Nath Das,et al.  An ideal tri-population approach for unconstrained optimization and applications , 2015, Appl. Math. Comput..

[40]  K. Benatchba,et al.  Parallel B&B Algorithm for Hybrid Multi-core/GPU Architectures , 2013, 2013 IEEE 10th International Conference on High Performance Computing and Communications & 2013 IEEE International Conference on Embedded and Ubiquitous Computing.

[41]  Ying Tan,et al.  Surrogate-assisted hierarchical particle swarm optimization , 2018, Inf. Sci..

[42]  Hugo Jair Escalante,et al.  A hybrid surrogate-based approach for evolutionary multi-objective optimization , 2013, 2013 IEEE Congress on Evolutionary Computation.

[43]  Hanna Zini,et al.  A discrete particle swarm optimization with combined priority dispatching rules for hybrid flow shop scheduling problem , 2015 .

[44]  Pawel B. Myszkowski,et al.  Hybrid ant colony optimization in solving multi-skill resource-constrained project scheduling problem , 2014, Soft Computing.

[45]  El-Ghazali Talbi,et al.  Parallel hybrid multi-objective island model in peer-to-peer environment , 2005, 19th IEEE International Parallel and Distributed Processing Symposium.