Modified Bat Algorithm Based on Lévy Flight and Opposition Based Learning

Abstract

Bat Algorithm (BA) is a swarm intelligence algorithm which has been intensively applied to solve academic and real life optimization problems. However, due to the lack of good balance between exploration and exploitation, BA sometimes fails at finding global optimum and is easily trapped into local optima. In order to overcome the premature problem and improve the local searching ability of Bat Algorithm for optimization problems, we propose an improved BA called OBMLBA. In the proposed algorithm, a modified search equation with more useful information from the search experiences is introduced to generate a candidate solution, and Lévy Flight random walk is incorporated with BA in order to avoid being trapped into local optima. Furthermore, the concept of opposition based learning (OBL) is embedded to BA to enhance the diversity and convergence capability. To evaluate the performance of the proposed approach, 16 benchmark functions have been employed. The results obtained by the experiments demonstrate the effectiveness and efficiency of OBMLBA for global optimization problems. Comparisons with some other BA variants and other state-of-the-art algorithms have shown the proposed approach significantly improves the performance of BA. Performances of the proposed algorithm on large scale optimization problems and real world optimization problems are not discussed in the paper, and it will be studied in the future work.

[Retracted] Taxonomy of Adaptive Neuro‐Fuzzy Inference System in Modern Engineering Sciences

“…Model hybrids ABC [57] aABC [43,58], adaptive ABC (AABC) [59], vortex search [60], cooperative ABC (CABC) [61,62], cooperative micro-ABC (CMABC) [63], interval cooperative multiobjective ABC (ICMOABC) [62], ABC-PSO [64], multiobjective directed bee colony optimization (MODBCO) [65], Scoutless ABC [35], directed ABC [66,67] ACO [68] ACOR [36], heuristic-PS-ACO (HPSACO) [69], hybrid ACO [70], ACO-PSO [71], PS-ACO [72], ACO-SA [73], MWIS-ACO-LS [74], hybrid ACO (HAntCO) [75], min-max ant System (MMAS) [72,76], GA-ACO-SA [77], self-adaptive ant colonygenetic hybrid [78], GA-ACO [79], ACS [80], greedy ACS [81] BA [82] Binary BA [83], hybrid BA with ABC [84], BA-HS [85], adaptive BA [86], adaptive multiswarm BA (AMBA) [87], binary BA [83], differential operator & Levy flights BA [87], directed artificial BA (DABA) [88], double-subpopulation Levy flight BA (DLBA) [89], dynamic virtual BA (DVBA) [90], improved DVBA with probabilistic selection [91], island multipopulational parallel BA (IBA)…”

Metaheurística Inspirada no Comportamento do Corvo com Multiestratégias de Aprendizagem Baseada em Oposição

“…Uma solução para esse problema é buscar, simultaneamente, em todas as direções ou, simplesmente, na direção oposta. Para lidar com esta situação, a Aprendizagem Baseada em Oposição (OBL -Opposition-Based Learning) [16] vem sendo utilizada por outras metaheurísticas [24][25] [26] Para melhorar o desempenho da OBL, diversas variantes foram propostas na literatura. Algumas delas são Quasi Opposition-Based Learning (QOBL) [27], Opposition-Based Learning using the Current Optimum (COOBL) [28], Elite Opposition-Based Learning (EOBL) [17] e Generalized Opposition-Based Learning (GOBL) [18], além de outras.…”

Taxonomy of Memory Usage in Swarm Intelligence-Based Metaheuristics

“…Most SI-based metaheuristics are imitations of the behavior ants, termites, bees, fish and birds (17). Some SI-based metaheuristics are inspired by the same creature with enhancement made for better performance (18)(19)(20)(21)(22)(23). However, the major difference between these metaheuristics is in the moving rules of individuals in the solutions space.…”