Improved Antlion Optimizer Algorithm and Its Performance on Neuro Fuzzy Inference System

Abstract

Antlion optimizer algorithm (ALO) is inspired by hunting strategy of antlions. In this study, an improved antlion optimization algorithm is proposed for training parameters of adaptive neuro fuzzy inference system (ANFIS). In the standard ALO algorithm, the greatest deficiency is its long running time during optimization process. The random walking model of ants, the selection procedure and boundary checking mechanism have been developed to speed up standard ALO algorithm. To evaluate the performance of the improved antlion optimization algorithm (IALO), it has been tested on dynamic system modelling problems. ANFIS's parameters has been optimized by IALO algorithm to model five dynamic systems. ANFIS training procedure has been performed with 30 independent runs. Each training has been started with the random initial parameters of ANFIS and performance metrics have been obtained at the end of training. The results show that the IALO algorithm is able to provide competitive results in terms of mean, best, worst, standard deviation, training time metrics. According to the training time result, the proposed IALO algorithm has better performance than standard ALO algorithm and the average training time has been reduced to approximately 80 %.

Improved Antlion Optimization Algorithm for Quadratic Assignment Problem

“…IALO result has the second best cost value as -1078209.911. In the results of all algorithms, facility pairs (19)(20), (11)(12)(13)(14)(15)(16) For QAP, the comparison results with 10 independent runs of IALO and the others are given in Table 7. This consists of mean cost, standard deviation, best cost and worst cost values from 10 runs.…”

“…There are some studies reported in the literature regarding applications or improvement of the ALO algorithm. Some of these are: PID controller parameters design [2], optimal non-convex and dynamic economic load [3], tournament selection based ALO algorithm for solving parallel machine scheduling [4] and quadratic assignment problem [5], optimal flexible process planning [6], optimal route planning for unmanned aerial vehicle [7], multi objective optimal generation scheduling [8], automatic generation control of interconnected power system [9], determining the optimal coefficients of IIR filters [10], and optimization of parameters on neuro-fuzzy inference system [11], [12].…”

“…(19,20) = 𝑤(20,19) = 10000 𝑤(11,16) = 𝑤(16,11) = 10000 𝑤(1,13) = 𝑤(13,1) = −10000The values of this matrix are given in the appendix section. The locations of this QAP instance are shown in Fig.9.…”

Implementation and comparative analysis of evolutionary algorithms for energy optimization in wireless sensor networks

“…They are still at the evolving stage and suffer from some problem. For example, the ant lion algorithm suffers from long run time, local optima stagnation 24,25 . Grey‐wolf algorithm has low solving accuracy and bad local searching ability.…”

Ant Lion Optimization: Variants, Hybrids, and Applications

“…In [120] Yu proposed an improved version of antlion by hybridizing ALO with Nelder-Mead algorithm and apply it to detect structural damage by improving weighted trace lasso regularization. Kilic et al [121] introduced IALO algorithm by using the absolute value of fitness value before applying roulette wheel selection. In addition to the above enhancement, Zhang et al in [122] showed another improved approach of ALO by integrating it chaotic mapping theory with initialization and random walk process.…”