TA的每日心情 | 衰
2019-11-19 15:32 |
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签到天数: 1 天 [LV.1]初来乍到
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一、源代码( B' u3 a- U, h. W4 S
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- %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
- %
- % Multi-Objective Golden Eagle Optimizer (MOGEO) source codes version 1.0
- %
- % Original paper: Abdolkarim Mohammadi-Balani, Mahmoud Dehghan Nayeri,
- % Adel Azar, Mohammadreza Taghizadeh-Yazdi,
- % Golden Eagle Optimizer: A nature-inspired
- % metaheuristic algorithm, Computers & Industrial Engineering.
- % To use this code in your own project
- % remove the line for 'GetFunctionDetails' function
- % and define the following parameters:
- % fun : function handle to the .m file containing the objective function
- % the .m file you define should accept 'x' as input and return
- % a column vector containing objective function values
- % nobj : number of objectives
- % nvars : number of decision/design variables
- % lb : lower bound of decision variables (must be of size 1 x nvars)
- % ub : upper bound of decision variables (must be of size 1 x nvars)
- %
- % MOGEO will return the following:
- % x : best solution found
- % fval : objective function value of the found solution
- %% Inputs
- FunctionNumber = 7; % 1-10
- options.PopulationSize = 200;
- options.ArchiveSize = 100;
- options.MaxIterations = 1000;
- options.FunctionNumber = FunctionNumber;
- %% Run Multi-Objective Golden Eagle Optimizer
- [fun,nobj,nvars,lb,ub] = GetFunctionDetails (FunctionNumber);
- options.AttackPropensity = [0.5 , 2];
- options.CruisePropensity = [1 , 0.5];
- [x,fval] = MOGEO (fun,nobj,nvars,lb,ub, options);
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4 U' I+ L- ~! I$ C2 j0 I% M1 I- I二、运行结果% N# d, L* Q! w' F
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发表于 2021-3-31 15:03
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