A Study on the Effect of Local Neighbourhood Parameter towards the Performance of SAFIRO
Keywords:Finite Impulse Response, Local Search Neighborhood, Metaheuristic, Optimization.
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, Î², is one of the parameters used in the local neighborhood to control the step size. In this study, the effect of Î² towards the performance of SAFIRO is observed by assigning the value of 1, 5, 10, 15, and 20. The best setting of Î² for SAFIRO is also determined. The CEC2014 Benchmark Test Suite is used to evaluate the SAFIRO performance with different Î² values. Results show that the performance of Î² is depending on the problems to be optimized. 17 out of 30 functions show the best performance of SAFIRO by setting Î² = 10. Statistical analysis using Friedman test and Holm post hoc test were performed to rank the performance. Î² = 10 has the highest rank where its performance is significantly better than other values, but equivalent to Î² = 5 and Î² = 15. Hence, it is recommended to tune the Î² for best performance, however, Î² = 10 is a good value to be used in SAFIRO for solving optimization problems.
 Kirkpatrick S, Gelatt CD, & Vecchi MP (1983), Optimization by Simulated Annealing, Science, Vol. 220, No. 4598, pp. 671â€“680.
 Glover F (1989), Tabu Search - Part I, ORSA J. Comput., Vol. 1, No. 3, pp. 190â€“206.
 Solis FJ & Wets RJB (1981), Minimization By Random Search Techniques, Math. Oper. Res., Vol. 6, No. 1, pp. 19â€“30.
 Michael Lewis R & Torczon V (1996), Pattern Search Algorithms for Bound Constrained Minimization.
 Feo TA & Resende MGC (1995), Greedy Randomized Adaptive Search Procedures, J. Glob. Optim., Vol. 6, pp. 109â€“133.
 Mladenovic N & Hansen P (1997), Variable Neighborhood Search, Comput. Oper. Res., Vol. 24, No. 11, pp. 1097â€“1100.
 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. 469â€“499.
 LourenÃ§o HR, Martin O, & StÃ¼tzle T, A Beginnerâ€™s Introduction to Iterated Local Search, Proceeding 4th Metaheuristics Int. Conf., June 2014, (2001), pp. 4-11.
 Boussaid I, Lepagnot J & Siarry P (2013), A Survey on Optimization Metaheuristics, Inf. Sci. (Ny)., Vol. 237, pp. 82â€“117.
 Yang XS (2015), Recent Advances in Swarm Intelligence and Evolutionary Computation, Springer International Publishing Switzerland.
 Yang XS, Deb S & Fong S (2014), Metaheuristic Algorithms: Optimal Balance of Intensification and Diversification, Appl. Math. Inf. Sci., Vol. 8, No. 3, pp. 977â€“983.
 Dumitrescu I & StÃ¼tzle T (2013), A Survey of Methods that Combine Local Search and Exact Algorithms, Appl. Evol. Comput., pp. 57â€“68.
 Osman IH & Laporte G (1996), Metaheuristics: A Bibliography, Ann. Oper. Res., Vol. 63, No. 5, pp. 511â€“623.
 HÃ©liodore F, Amir N, Ismail B, Ouchraa S & Schmitt L (2017), Metaheuristics for Intelligent Electrical Networks, ISTE Ltd and John Wiley & Sons, Inc.
 Bertsimas D & Tsitsiklis J (1993), Simulated Annealing, Statistical Science, Vol. 8, No. 1. pp. 10â€“15.
 Blum C & Roli A (2003), Metaheuristics in Combinatorial Optimization : Overview and Conceptual Comparison, Vol. 35, No. 3, pp. 268â€“308.
 Ibrahim A, Rahnamayan S & Martin MV, Simulated Raindrop Algorithm for Global Optimization, Canadian Conference on Electrical and Computer Engineering, (2014), pp. 1â€“8.
 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. 1â€“12.
 Sevaux M, SÃ¶rensen K & Glover F (2016), Metaheuristics, October 2016.
 Beheshti Z & Shamsuddin SM (2013), A Review of Population-based Meta-Heuristic Algorithms, Int. J. Adv. Soft Comput. Appl., Vol. 5, No. 1, pp. 1â€“35.
 Saremi S, Mirjalili S & Lewis A (2017), Grasshopper Optimisation Algorithm: Theory and Application, Adv. Eng. Softw., Vol. 105, pp. 30â€“47.
 Glover F & Kochenberger G (2003), Handbook of Metaheuristics, Kluwer Academic Publishers.
 Dogan B & Olmez T (2015), A New Metaheuristic for Numerical Function Optimization: Vortex Search Algorithm, Inf. Sci. (Ny)., Vol. 293, pp. 125â€“145.
 Rueda JL & Erlich I, MVMO for Bound Constrained Single-objective Computationally Expensive Numerical Optimization,â€ 2015 IEEE Congress on Evolutionary Computation, (2015), pp. 1011â€“1017.
 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. 2333â€“2335.
 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.
 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.
 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.
 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.
 Mirjalili S, Mirjalili SM & Lewis A (2014), Grey Wolf Optimizer, Adv. Eng. Softw., Vol. 69, pp. 46-61.
 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.