![]() Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. IEEE Trans Evol Comput 3:82–102Įrol OK, Eksin I (2006) A new optimization method: big bang–big crunch. Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. Storn R, Price K (1997) Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Goldberg DE, Holland JH (1988) Genetic algorithms and machine learning. Springer International Publishing, Cham, pp 243–253 In: Shankar BU, Ghosh K, Mandal DP, Ray SS, Zhang D, Pal SK (eds) Pattern recognition and machine intelligence. Singh P, Dhiman G (2017) A fuzzy-lp approach in time series forecasting. In: Proceedings of Sixth International Conference on Soft Computing for Problem Solving. The simulation results of the real-life engineering design problems exhibit the superiority of the improved PFA (IMPFA) algorithm in solving challenging problems with constrained and unknown search spaces when compared to the basic PFA algorithm or other available solutions.Ĭhandrawat RK, Kumar R, Garg BP, Dhiman G, Kumar S (2017) An analysis of modeling and optimization production cost through fuzzy linear programming problem with symmetric and right angle triangular fuzzy number. ![]() To verify the performance of the improved algorithm, it is applied to nine real-life engineering case problems. To further balance the mining ability and exploration ability of the algorithm, the article regards the leader as a guide and introduces a guide mechanism. To overcome this shortcoming, the following stage is complicated in this paper, and the acceptance operator, the exchange operator and the mutation mechanism are introduced into the algorithm. ![]() In PFA, followers follow the new position according to the position of the leader and their own consciousness makes the algorithm easy to fall into local optimum. The pathfinder algorithm (PFA) is a new population-based optimizer, it divides the search agents of the algorithm into leaders and followers, imitating the leadership level of the group movement to find the best food area or prey. ![]()
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