The effect of elite pool in hybrid population-based meta-heuristics for solving combinatorial optimization problems

This work investigates the effect of elite pool that has high-quality and diverse solutions in three hybrid population-based meta-heuristics with an elite pool of a hybrid Elitist-Ant System, a hybrid Big Bang-Big Crunch optimization, and a hybrid scatter search. The purpose of incorporating an elite pool in population-based meta-heuristics is to maintain the diversity of the search while exploiting the solution space as in the reference set of the scatter search. This may guarantee the effectiveness and efficiency of the search, which could enhance the performance of the algorithms and generalized well across different datasets. To test the generality of these meta-heuristics via their consistency and efficiency, we use three classes of well-known combinatorial optimization problems as follows: symmetric traveling salesman problem, 0-1 multidimensional knapsack problem, and capacitated vehicle routing problem. Experimental results showed that the performance of our hybrid population-based meta-heuristics, compared to the best known results, is competitive in many instances. This finding indicates the effectiveness of utilizing an elite pool in our hybrid meta-heuristics in diversifying the search and subsequently enhances their performance over different instances and problems.

[1]  Yves Rochat,et al.  Probabilistic diversification and intensification in local search for vehicle routing , 1995, J. Heuristics.

[2]  Manuel Laguna.  Scatter Search and Path Relinking , 2009, EMO.

[3]  W. Y. Szeto,et al.  An artificial bee colony algorithm for the capacitated vehicle routing problem , 2011, Eur. J. Oper. Res..

[4]  Paolo Toth,et al.  A new lower bound for curriculum-based course timetabling , 2013, Comput. Oper. Res..

[5]  Michel Gendreau,et al.  Path relinking for the vehicle routing problem , 2004, J. Heuristics.

[6]  José Luis Montaña,et al.  A Flipping Local Search Genetic Algorithm for the Multidimensional 0-1 Knapsack Problem , 2005, CAEPIA.

[7]  Rafael Bello,et al.  Two-Stage Ant Colony Optimization for Solving the Traveling Salesman Problem , 2007, IWINAC.

[8]  Olli Bräysy,et al.  Active-guided evolution strategies for large-scale capacitated vehicle routing problems , 2007, Comput. Oper. Res..

[9]  Gilbert Laporte,et al.  The vehicle routing problem: An overview of exact and approximate algorithms , 1992 .

[10]  Gerhard Reinelt,et al.  TSPLIB - A Traveling Salesman Problem Library , 1991, INFORMS J. Comput..

[11]  Éric D. Taillard,et al.  Parallel iterative search methods for vehicle routing problems , 1993, Networks.

[12]  El-Ghazali Talbi,et al.  Metaheuristics - From Design to Implementation , 2009 .

[13]  Ibrahim Eksin,et al.  A new optimization method: Big Bang-Big Crunch , 2006, Adv. Eng. Softw..

[14]  Ángel Corberán,et al.  Scatter search , 2003 .

[15]  Hsin-Ginn Hwang,et al.  Key factors affecting healthcare professionals to adopt knowledge management: The case of infection control departments of Taiwanese hospitals , 2010, Expert Systems with Applications.

[16]  Masri Ayob,et al.  Effect of Elite Pool and Euclidean Distance in Big Bang-Big Crunch Metaheuristic for Post-Enrolment Course TimetablingProblems , 2013 .

[17]  G. Clarke,et al.  Scheduling of Vehicles from a Central Depot to a Number of Delivery Points , 1964 .

[18]  H.M. Genc,et al.  Bearing-Only Target Tracking Based on Big Bang – Big Crunch Algorithm , 2008, 2008 The Third International Multi-Conference on Computing in the Global Information Technology (iccgi 2008).

[19]  Magdalene Marinaki,et al.  A hybrid genetic - Particle Swarm Optimization Algorithm for the vehicle routing problem , 2010, Expert Syst. Appl..

[20]  Edmund K. Burke,et al.  Effective learning hyper-heuristics for the course timetabling problem , 2014, Eur. J. Oper. Res..

[21]  Masri Ayob,et al.  An Elitist-Ant System for Solving the Post-Enrolment Course Timetabling Problem , 2010, FGIT-DTA/BSBT.

[22]  Chunguo Wu,et al.  Solving traveling salesman problems using generalized chromosome genetic algorithm , 2008 .

[23]  Novruz Allahverdi,et al.  Development a new mutation operator to solve the Traveling Salesman Problem by aid of Genetic Algorithms , 2011, Expert Syst. Appl..

[24]  Zulkifli Ahmad,et al.  On the performance of Scatter Search for post-enrolment course timetabling problems , 2014, J. Comb. Optim..

[25]  Dervis Karaboga,et al.  A combinatorial Artificial Bee Colony algorithm for traveling salesman problem , 2011, 2011 International Symposium on Innovations in Intelligent Systems and Applications.

[26]  Celso C. Ribeiro,et al.  Scatter Search and Path-Relinking: Fundamentals, Advances, and Applications , 2010 .

[27]  Graham Kendall,et al.  An Ant Based Hyper-heuristic for the Travelling Tournament Problem , 2007, 2007 IEEE Symposium on Computational Intelligence in Scheduling.

[28]  Saïd Hanafi,et al.  Scatter Search for the 0–1 Multidimensional Knapsack Problem , 2008, J. Math. Model. Algorithms.

[29]  José Rui Figueira,et al.  A Scatter Search Method for the Bi-Criteria Multi-dimensional {0,1}-Knapsack Problem using Surrogate Relaxation , 2004, J. Math. Model. Algorithms.

[30]  Juan José Pantrigo,et al.  Scatter Search Particle Filter to Solve the Dynamic Travelling Salesman Problem , 2005, EvoCOP.

[31]  Graham Kendall,et al.  Grammatical Evolution Hyper-Heuristic for Combinatorial Optimization Problems , 2013, IEEE Transactions on Evolutionary Computation.

[32]  Graham Kendall,et al.  A Dynamic Multiarmed Bandit-Gene Expression Programming Hyper-Heuristic for Combinatorial Optimization Problems , 2015, IEEE Transactions on Cybernetics.

[33]  Ben Paechter,et al.  A Comparison of the Performance of Different Metaheuristics on the Timetabling Problem , 2002, PATAT.

[34]  Lipo Wang,et al.  Ant Colony Optimization for the Traveling Salesman Problem Based on Ants with Memory , 2008, 2008 Fourth International Conference on Natural Computation.

[35]  Nicos Christofides,et al.  The vehicle routing problem , 1976, Revue française d'automatique, informatique, recherche opérationnelle. Recherche opérationnelle.

[36]  Sara Ceschia,et al.  Tabu search techniques for the heterogeneous vehicle routing problem with time windows and carrier-dependent costs , 2011, J. Sched..

[37]  Mohammed Azmi Al-Betar,et al.  University course timetabling using hybridized artificial bee colony with hill climbing optimizer , 2014, J. Comput. Sci..

[38]  Rong Qu,et al.  Computational Study for Workforce Scheduling and Routing Problems , 2014, ICORES.

[39]  Rubén Ruiz,et al.  The effect of the asymmetry of road transportation networks on the traveling salesman problem , 2012, Comput. Oper. Res..

[40]  Marcus Randall,et al.  The Accumulated Experience Ant Colony for the Traveling Salesman Problem , 2003, Int. J. Comput. Intell. Appl..

[41]  John E. Beasley,et al.  OR-Library: Distributing Test Problems by Electronic Mail , 1990 .

[42]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[43]  Edmund K. Burke,et al.  A survey of search methodologies and automated system development for examination timetabling , 2009, J. Sched..

[44]  Nasser R. Sabar,et al.  Multi-parent insertion crossover for vehicle routing problem with time windows , 2012, 2012 4th Conference on Data Mining and Optimization (DMO).

[45]  Graham Kendall,et al.  Automating the Packing Heuristic Design Process with Genetic Programming , 2012, Evolutionary Computation.

[46]  Thomas Stützle,et al.  Ant Colony Optimization: Overview and Recent Advances , 2018, Handbook of Metaheuristics.

[47]  Thomas Stützle,et al.  Stochastic Local Search: Foundations & Applications , 2004 .

[48]  El-Ghazali Talbi,et al.  A Taxonomy of Hybrid Metaheuristics , 2002, J. Heuristics.