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The multiobjective bat algorithm (MOBA) is a nature-inspired optimization algorithm. This demo solves the bi-objective ZDT3 functions with D=30 (dimensions), and the obtained Pareto Front is displayed. It is relatively straightforward to extend this code to solve other multi-objective functions and optimization problems. You can change the objective functions, the dimensionality, and simple lower and upper bounds (Lb, Ub) as well as certain parameters. Yang, Xin She. “Bat Algorithm for Multi-Objective Optimisation.” International Journal of Bio-Inspired Computation, vol. 3, no. 5, Inderscience Publishers, 2011, p. 267, doi:10.1504/ijbic.2011.042259. [Notes: Though this demo should work well using either Matlab (preferred) or Octave (free), Matlab can run more smoothly, whereas Octave can be slower. In addition, for the multi-objective codes, Octave can be very slow for the test problem with 30 dimensions given in the demo codes, so please modify the relevant part of the codes to display results more frequently to show the progress. At the moment, the results are displayed every 100 iterations.]
Engineering optimization is typically multiobjective and multidisciplinary with complex constraints, and the solution of such complex problems requires efficient optimization algorithms. Recently, Xin-She Yang proposed a bat-inspired algorithm for solving nonlinear, global optimisation problems. In this paper, we extend this algorithm to solve multiobjective optimisation problems. The proposed multiobjective bat algorithm (MOBA) is first validated against a subset of test functions, and then applied to solve multiobjective design problems such as welded beam design. Simulation results suggest that the proposed algorithm works efficiently.
Despite the fact that algorithmic strategy for dealing with Combinatorial Optimization (OP) have been available for a long time, the further application of Evolutionary Algorithms (EAs) to such problems provides a vehicle for dealing with MOPs of tremendous scope. BAT Algorithm with Many Objectives A several BAT algorithms based on R2 Distance (MaBAT/R2) is described, which blends the predominance notion with the R2 marker technique. While the R2 Indicator simplifies the multi-objective problem (MOP) by rewriting it as a series of Tchebycheff Approach problems, since this leader decision making uses the Tchebycheff Approach as a criterion, tackling these issues at the same time inside the BAT framework may lead to early converging. Predominance is important in constructing the leader's collection because it allows the chosen leaders to encompass fewer dense regions, avoiding local optima and producing a more diverse approximated Pareto front. 9 non-linear standard functions yielded this result. MaBAT/R2 appears to be more efficient than MOEAD, NSGAII, MPSOD, and SPEA2. MATLAB was used to generate all of the findings (R2020b).
2022 •
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2004 •
2018 •
Mathematical Methods of Operations Research
FEMOEA: a fast and efficient multi-objective evolutionary algorithm2016 •
2017 •
MOGSABAT
MOGSABAT: a metaheuristic hybrid algorithm for solving multi- objective optimisation problems2018 •
This study proposes a novel strength of multi-objective gravitational search algorithm and bat algorithm MOGSABAT to solve multi-objective optimisation problem. The proposed MOGSABAT algorithm is divided into three stages. In the first stage (moving space), a switch in a solution from single function to multiple functions that contain more than one objective to use the gravitational search algorithm GSA is determined. We established a new equation to calculate the masses of individuals in the population using the theoretical work found in the strength Pareto evolutionary algorithm. In the second stage (moving in space), how to handle the bat algorithm BAT to solve multiple functions is established. We applied the theoretical work of multi-objective particle swarm optimisation into the BAT algorithm to solve multiple functions. In the third stage, multi-objective GSA and multi-objective BAT are integrated to obtain the hybrid MOGSABAT algorithm. MOGSABAT is tested by adopting a three-part evaluation methodology that (1) describes the benchmarking of the optimisation problem (bi-objective and tri-objective) to evaluate the performance of the algorithm; (2) compares the performance of the algorithm with that of other intelligent computation techniques and parameter settings; and (3) evaluates the algorithm based on mean, standard deviation and Wilcoxon signed-rank test statistic of the function values. The optimisation results and discussion confirm that the MOGSABAT algorithm competes well with advanced metaheuristic algorithms and conventional methods.
International Journal of Intelligent Engineering Informatics
Unconstrained optimisation through bat algorithm2014 •
Computers & Industrial Engineering
Multi-objective chicken swarm optimization: A novel algorithm for solving multi-objective optimization problems2019 •
2013 •
Science Publishing Corporation
Bat algorithm (BA): review, applications and modifications2020 •
Advanced Information and Knowledge Processing
A Simple Approach to Evolutionary Multiobjective Optimization2005 •
Studies in Computational Intelligence
ParadisEO-MOEO: A Software Framework for Evolutionary Multi-Objective Optimization2010 •
Mathematical Problems in Engineering
A Modified Bat Algorithm for Solving Large-Scale Bound Constrained Global Optimization ProblemsApplied Soft Computing
A new modification approach on bat algorithm for solving optimization problems2015 •
Neural Computing and Applications
Recent advances of bat-inspired algorithm, its versions and applications2017 •
Innovative Design, Analysis and Development Practices in Aerospace and Automotive Engineering
TLBO and JAYA: Insights into Novel Multi-objective Optimization Techniques2020 •
Zenodo (CERN European Organization for Nuclear Research)
Multi-objective optimization approach based on Minimum Population Search algorithmInternational Journal of Fuzzy System Applications
A Novel Fuzzy Inspired Bat Algorithm for Multidimensional Function Optimization Problem2019 •
2000 •
2022 •
International Journal of Advanced Research in Computer Science and Software Engineering
An Improved Approach for Bat Algorithm