?1 內容介紹

tled attacker, barrier, chaser, and driver are employed for simulating the diverse intelligence. Moreover, the four main steps of hunting, driving, blocking, and attacking, are implemented. Afterward, the algorithm is tested on 30 well-known benchmark functions, and the results are compared to four newly proposed meta-heuristic algorithms in term of convergence speed, the probability of getting stuck in local minimums, and the accuracy of obtained results. The results indicate that the ChOA outperforms the other benchmark optimization algorithms.

?

2 仿真代碼

function [new_pops] = NondominatedSort_and_filling(pop, nobj, ncon, nreal, nbin)
N = size(pop,1);
fitsize = N/2;
[sorted_ranks, rankID] = sort(pop(:,nobj+ncon+nreal+nbin+2));
parentID = []; front = 1; front_array = [];
while size(parentID,2) < fitsize
? ? for i=1:N
? ? ? ? if sorted_ranks(i) ~= front
? ? ? ? ? ? break
? ? ? ? end
? ? ? ? front_array = [front_array rankID(i)];
? ? end
? ? size_check = size(parentID,2) + size(front_array,2);
? ? if size_check == fitsize
? ? ? ? parentID = [parentID front_array];
? ? ? ? break
? ? elseif size_check < fitsize
? ? ? ? parentID = [parentID front_array];
? ? ? ? sorted_ranks(1:size(front_array,2)) = [];
? ? ? ? rankID(1:size(front_array,2)) = [];
? ? ? ? N = N - size(front_array,2);
? ? ? ? front = front+1;
? ? ? ? front_array = [];
? ? else
? ? ? ? miss_size = fitsize - (size_check - size(front_array,2));
? ? ? ? n_consviol = nobj+ncon+nreal+nbin+1;
? ? ? ? n_rank= nobj+ncon+nreal+nbin+2;
? ? ? ? n_crowd = n_rank+1;

? ? ? ? if ncon==0,% if the problem does not have any constraints
? ? ? ? ? ?
? ? ? ? ? ? % sort crowding distance and select miss_size number of top individuals ?
? ? ? ? ? ? % and add them to the parent pop
? ? ? ? ? ? [~, distID] = sort(pop(front_array,n_crowd), 'descend');
? ? ? ? ? ? parentID = [parentID front_array(distID(1:miss_size))];
? ? ? ? ? ??
? ? ? ? else % if the problem have constraints
? ? ? ? ? ??
? ? ? ? ? ? %calculate the number of the feasible individuals
? ? ? ? ? ? feasible_ind=find(pop(front_array,n_consviol)==0);
? ? ? ? ? ? feasible_ind=(front_array(feasible_ind));
? ? ? ? ? ? number_feasible_ind=length(feasible_ind);
? ? ? ? ? ??
? ? ? ? ? ? if ?number_feasible_ind > miss_size, ??
? ? ? ? ? ? ? ? % sort feasible individuals based on crowdind distance?
? ? ? ? ? ? ? ? % select the best miss_size of them and add them to parent
? ? ? ? ? ? ? ? % pop
? ? ? ? ? ? ? ? [~, distID] = sort(pop(feasible_ind,n_crowd), 'descend');
? ? ? ? ? ? ? ? parentID = [parentID feasible_ind(distID(1:miss_size))]; ? ? ??
? ? ? ? ? ? elseif number_feasible_ind == miss_size,
? ? ? ? ? ? ? ? parentID = [parentID feasible_ind];?
? ? ? ? ? ? else % where number_feasible_ind < miss_size,
? ? ? ? ? ? ? ? % sort based on the constraint violation,
? ? ? ? ? ? ? ? [~, consviolationID] = sort(pop(front_array,n_consviol), 'descend');
? ? ? ? ? ? ? ? parentID = [parentID front_array(consviolationID(1:miss_size))];
? ? ? ? ? ? end
? ? ? ? end
? ? end
end
new_pops = pop(parentID,:);
end

function ab=SelectAlphaBonobo(pop,ncolumn)
[d2,ind]=sort(pop(:,ncolumn),'descend');
d2(1:2)=d2(3);
d2=d2/(sum(d2));
r=rand;
C=cumsum(d2);
i=find(r<=C,1,'first');
ab=pop(ind(i),:);
end

function ab=SelectAlphaBonobo(pop,ncolumn)
[d2,ind]=sort(pop(:,ncolumn),'descend');
d2(1:2)=d2(3);
d2=d2/(sum(d2));
r=rand;
C=cumsum(d2);
i=find(r<=C,1,'first');
ab=pop(ind(i),:);
end

3 運行結果

4 參考文獻

[1]程國森, and 崔東文. "黑猩猩優化算法-極限學習機模型在富水性分級判定中的應用." 人民黃河 43.7(2021):6.

[2]劉成漢, and 何慶. "融合多策略的黃金正弦黑猩猩優化算法." 自動化學報 47(2021):1-14.

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