【智能优化算法-鲸鱼算法】基于鲸鱼算法求解多目标优化问题附matlab代码(NSWOA)

1 内容介绍

为了解决多目标优化的相关问题,鲸鱼优化算法结合多目标相关理论,并在算法中加入了非排序思路,提出了一种求解多目标问题的鲸鱼优化算法.

2 仿真代码

%% Non Sorted Whale Optimization Algorithm (NSWOA)

% NSWOA is developed by Pradeep Jangir

%% Objective Function

% The objective function description contains information about the

% objective function. M is the dimension of the objective space, D is the

% dimension of decision variable space, LB and UB are the

% range for the variables in the decision variable space. User has to

% define the objective functions using the decision variables. Make sure to

% edit the function 'evaluate_objective' to suit your needs.

clc

clear all

D = 30; % Number of decision variables

M = 2; % Number of objective functions

K=M+D;

LB = ones(1, D).*0; % LB - A vector of decimal values which indicate the minimum value for each decision variable.

UB = ones(1, D).*1; % UB - Vector of maximum possible values for decision variables.

Max_iteration = 100; % Set the maximum number of generation (GEN)

SearchAgents_no = 100; % Set the population size (Search Agent)

ishow = 10;

%% Initialize the population

% Population is initialized with random values which are within the

% specified range. Each chromosome consists of the decision variables. Also

% the value of the objective functions, rank and crowding distance

% information is also added to the chromosome vector but only the elements

% of the vector which has the decision variables are operated upon to

% perform the genetic operations like corssover and mutation.

chromosome = initialize_variables(SearchAgents_no, M, D, LB, UB);

%% Sort the initialized population

% Sort the population using non-domination-sort. This returns two columns

% for each individual which are the rank and the crowding distance

% corresponding to their position in the front they belong. At this stage

% the rank and the crowding distance for each chromosome is added to the

% chromosome vector for easy of computation.

intermediate_chromosome = non_domination_sort_mod(chromosome, M, D);

%% Perform Selection

% Once the intermediate population is sorted only the best solution is

% selected based on it rank and crowding distance. Each front is filled in

% ascending order until the addition of population size is reached. The

% last front is included in the population based on the individuals with

% least crowding distance

% Select NP fittest solutions using non dominated and crowding distance

% sorting and store in population

Population = replace_chromosome(intermediate_chromosome, M,D,SearchAgents_no);

%% Start the evolution process

% The following are performed in each generation

% * Select the parents which are fit for reproduction

% * Perfrom crossover and Mutation operator on the selected parents

% * Perform Selection from the parents and the offsprings

% * Replace the unfit individuals with the fit individuals to maintain a

% constant population size.

Pareto = NSWOA(D,M,LB,UB,Population,SearchAgents_no,Max_iteration,ishow);

save Pareto.txt Pareto -ascii; % save data for future use

%% Plot data

if M == 2

plot_data2(M,D,Pareto)

elseif M == 3

plot_data_TCQ(M,D,Pareto);

end

3 运行结果

4 参考文献

[1]滕德云, 滕欢, 刘鑫,等. 基于改进鲸鱼优化算法的多目标无功优化调度[J]. 电力电容器与无功补偿, 2019, 40(3):7.

[2]梁倩. 基于反向精英保留和Levy变异的多目标鲸鱼优化算法[J]. 现代计算机, 2021(18):7.

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