A Study on the Effect of Local Neighbourhood Parameter towards the Performance of SAFIRO

  • Abstract
  • Keywords
  • References
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  • Abstract

    Single-agent Finite Impulse Response Optimizer (SAFIRO) is a recently proposed metaheuristic optimization algorithm which adopted the procedure of the ultimate unbiased finite impulse response filter (UFIR) in state estimation. In SAFIRO, a random mutation with shrinking local neighborhood method is employed during measurement phase to balance the exploration and the exploitation process. Beta, β, is one of the parameters used in the local neighborhood to control the step size. In this study, the effect of β towards the performance of SAFIRO is observed by assigning the value of 1, 5, 10, 15, and 20. The best setting of β for SAFIRO is also determined. The CEC2014 Benchmark Test Suite is used to evaluate the SAFIRO performance with different β values. Results show that the performance of β is depending on the problems to be optimized. 17 out of 30 functions show the best performance of SAFIRO by setting β = 10. Statistical analysis using Friedman test and Holm post hoc test were performed to rank the performance. β = 10 has the highest rank where its performance is significantly better than other values, but equivalent to β = 5 and β = 15. Hence, it is recommended to tune the β for best performance, however, β = 10 is a good value to be used in SAFIRO for solving optimization problems.



  • Keywords

    Finite Impulse Response; Local Search Neighborhood; Metaheuristic; Optimization.

  • References

      [1] Kirkpatrick S, Gelatt CD, & Vecchi MP (1983), Optimization by Simulated Annealing, Science, Vol. 220, No. 4598, pp. 671–680.

      [2] Glover F (1989), Tabu Search - Part I, ORSA J. Comput., Vol. 1, No. 3, pp. 190–206.

      [3] Solis FJ & Wets RJB (1981), Minimization By Random Search Techniques, Math. Oper. Res., Vol. 6, No. 1, pp. 19–30.

      [4] Michael Lewis R & Torczon V (1996), Pattern Search Algorithms for Bound Constrained Minimization.

      [5] Feo TA & Resende MGC (1995), Greedy Randomized Adaptive Search Procedures, J. Glob. Optim., Vol. 6, pp. 109–133.

      [6] Mladenovic N & Hansen P (1997), Variable Neighborhood Search, Comput. Oper. Res., Vol. 24, No. 11, pp. 1097–1100.

      [7] Voudouris C & Tsang E (1999), Guided Local Search and its Application to the Traveling Salesman Problem, Eur. J. Oper. Res., Vol. 113, No. 2, pp. 469–499.

      [8] Lourenço HR, Martin O, & Stützle T, A Beginner’s Introduction to Iterated Local Search, Proceeding 4th Metaheuristics Int. Conf., June 2014, (2001), pp. 4-11.

      [9] Boussaid I, Lepagnot J & Siarry P (2013), A Survey on Optimization Metaheuristics, Inf. Sci. (Ny)., Vol. 237, pp. 82–117.

      [10] Yang XS (2015), Recent Advances in Swarm Intelligence and Evolutionary Computation, Springer International Publishing Switzerland.

      [11] Yang XS, Deb S & Fong S (2014), Metaheuristic Algorithms: Optimal Balance of Intensification and Diversification, Appl. Math. Inf. Sci., Vol. 8, No. 3, pp. 977–983.

      [12] Dumitrescu I & Stützle T (2013), A Survey of Methods that Combine Local Search and Exact Algorithms, Appl. Evol. Comput., pp. 57–68.

      [13] Osman IH & Laporte G (1996), Metaheuristics: A Bibliography, Ann. Oper. Res., Vol. 63, No. 5, pp. 511–623.

      [14] Héliodore F, Amir N, Ismail B, Ouchraa S & Schmitt L (2017), Metaheuristics for Intelligent Electrical Networks, ISTE Ltd and John Wiley & Sons, Inc.

      [15] Bertsimas D & Tsitsiklis J (1993), Simulated Annealing, Statistical Science, Vol. 8, No. 1. pp. 10–15.

      [16] Blum C & Roli A (2003), Metaheuristics in Combinatorial Optimization : Overview and Conceptual Comparison, Vol. 35, No. 3, pp. 268–308.

      [17] Ibrahim A, Rahnamayan S & Martin MV, Simulated Raindrop Algorithm for Global Optimization, Canadian Conference on Electrical and Computer Engineering, (2014), pp. 1–8.

      [18] Idoumghar L, Melkemi M, Schott R, and Aouad MI (2011), Hybrid PSO-SA Type Algorithms for Multimodal Function Optimization and Reducing Energy Consumption in Embedded Systems, Appl. Comput. Intell. Soft Comput., Vol. 2011, pp. 1–12.

      [19] Sevaux M, Sörensen K & Glover F (2016), Metaheuristics, October 2016.

      [20] Beheshti Z & Shamsuddin SM (2013), A Review of Population-based Meta-Heuristic Algorithms, Int. J. Adv. Soft Comput. Appl., Vol. 5, No. 1, pp. 1–35.

      [21] Saremi S, Mirjalili S & Lewis A (2017), Grasshopper Optimisation Algorithm: Theory and Application, Adv. Eng. Softw., Vol. 105, pp. 30–47.

      [22] Glover F & Kochenberger G (2003), Handbook of Metaheuristics, Kluwer Academic Publishers.

      [23] Dogan B & Olmez T (2015), A New Metaheuristic for Numerical Function Optimization: Vortex Search Algorithm, Inf. Sci. (Ny)., Vol. 293, pp. 125–145.

      [24] Rueda JL & Erlich I, MVMO for Bound Constrained Single-objective Computationally Expensive Numerical Optimization,” 2015 IEEE Congress on Evolutionary Computation, (2015), pp. 1011–1017.

      [25] Abdul Aziz NH, Ibrahim Z, Ab. Aziz NA, Mohamad MS & Watada J (2018), Single-solution simulated Kalman filter algorithm for global optimisation problems, Sadhana, Vol. 123, No. 4, pp. 2333–2335.

      [26] Ibrahim Z, Abdul Aziz NH, Ab. Aziz NA, Razali S, Shapiai MI, Nawawi SW & Mohamad MS (2015), A Kalman Filter Approach for Solving Unimodal Optimization Problems, ICIC Express Letters, Vol. 9, Issue 12, pp. 3415-3422.

      [27] Ibrahim Z, Abdul Aziz NH, Ab. Aziz NA, Razali R & Mohamad MS (2016), Simulated Kalman Filter: A Novel Estimation-Based Metaheuristic Optimization Algorithm, Advance Science Letters, Vol. 22, pp. 2941-2946.

      [28] Ab Rahman T, Ibrahim Z, Ab. Aziz NA, Zhao S & Abdul Aziz NH (2018), Single-Agent Finite Impulse Response Optimizer for Numerical Optimization Problems, IEEE Access, Vol. 6, pp. 9358-9374.

      [29] Liang JJ, Qu BY & Suganthan PN (2013), Problem Definitions and Evaluation Criteria for the CEC 2014 Special Session and Competition on Single Objective Real-Parameter Numerical Optimization.

      [30] Mirjalili S, Mirjalili SM & Lewis A (2014), Grey Wolf Optimizer, Adv. Eng. Softw., Vol. 69, pp. 46-61.

      [31] Ab. Aziz NA, Mubin M, Ibrahim Z & Nawawi SW (2015), Statistical Analysis for Swarm Intelligence - Simplified, Int. J. Futur. Comput. Commun., Vol. 4, No. 3, pp. 193-197, 2015.




Article ID: 22476
DOI: 10.14419/ijet.v7i4.27.22476

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