?作者簡介：熱愛科研的Matlab仿真開發(fā)者，修心和技術(shù)同步精進(jìn)，matlab項(xiàng)目合作可私信。

??個人主頁：Matlab科研工作室

??個人信條：格物致知。

? 內(nèi)容介紹

這篇新穎的文章介紹了最近提出的蛾火焰優(yōu)化器 (MFO) 的多目標(biāo)版本，稱為非支配排序蛾火焰優(yōu)化器 (NSMFO)。所提出的 NSMFO 算法的工作方式是，它首先收集所有非支配 Pareto 最優(yōu)解，直到最后一次迭代極限的演化。然后使用基于解決方案覆蓋范圍的擁擠距離機(jī)制作為導(dǎo)航策略，從所有 Pareto 最優(yōu)解的集合中選擇最佳解決方案，以引導(dǎo)飛蛾朝向多目標(biāo)搜索空間的主導(dǎo)區(qū)域。為了驗(yàn)證所提出的 NSMFO 算法的效率和有效性，將其應(yīng)用于一組標(biāo)準(zhǔn)的無約束、約束和工程設(shè)計(jì)問題。

? 部分代碼

%% Non Sorted Moth-flame?Optimization?Algorithm (NSMFO)

% NSMFO 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 = NSMFO(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

? 運(yùn)行結(jié)果

編輯

? 參考文獻(xiàn)

Pradeep J, Indrajit N T. Non-Dominated Sorting Moth Flame Optimizer: A Novel Multi-Objective Optimization Algorithm for Solving Engineering Design Problems. Eng Technol Open Acc。2018; 2(1): 555579. 10.19080/ETOAJ.2018.02.555579

? 完整代碼

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