Bat algorithm for multi-objective optimisation

By Xin-She Yang. International Journal of Bio-Inspired Computation (IJBIC), 2011

843 citations recorded by the source corpus.

Engineering optimisation is typically multi-objective and multidisciplinary with complex constraints, and the solution of such complex problems requires efficient optimisation algorithms. Recently, Xin-She Yang proposed a bat-inspired algorithm for solving non-linear, global optimisation problems. In this paper, we extend this algorithm to solve multi-objective optimisation problems. The proposed multi-objective bat algorithm (MOBA) is first validated against a subset of test functions, and then applied to solve multi-objective design problems such as welded beam design. Simulation results suggest that the proposed algorithm works efficiently.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/4f41fbee1998e4d3af72c2910a6ccb310f9a4bab

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computational Theory and Mathematics

In Statistics and Probability

Citations in the corpus

Metaheuristic algorithms for PID controller parameters tuning: review, approaches and open problems (2022, background): “2012 Flower Pollination Algorithm (FPA) Yang (2012b) 42.”
BBA: A Binary Bat Algorithm for Feature Selection (2012, methodology): “Algorithm 1 presents the Bat Algorithm (adapted from [12]): Initialize the bat population 𝑥 𝑖 and 𝑣 𝑖 , 𝑖 = 1 , 2 , ..., 𝑚 .”
Bat algorithm: literature review and applications (2013, methodology): “Many variants of PSO and improvements exist in the literature, and many new metaheuristic algorithms have been developed (Cui, 2009; Yang, 2010; Yang and Deb, 2010; Yang et al., 2011; Yang et al., 2013).”
Flower pollination algorithm: A novel approach for multiobjective optimization (2014, methodology): “FPA has the advantages such as simplicity and ﬂexibility, and in many ways, it has some similarity to that of cuckoo search and other algorithms with L´evy ﬂights (Yang 2010a, 2011b), however, it is still not clear that why FPA works well.”
Bio inspired computing - A review of algorithms and scope of applications (2016, methodology): “The bat algorithm (Yang, 2010; Yang, 2011) is one of the recently developed algorithm which uses the echo based location determining behaviour of bats to solve both single objective and multi-objective optimization problems in the continuous solution domain.”
Multi-Objective Flower Algorithm for Optimization (2014, positive): “Nature-inspired algorithms have shown their promising performance and have thus become popular and widely used, and these algorithms are mostly swarm intelligence based [4, 8, 22, 29, 30, 31, 32, 12, 13].”