季节优化算法（Seasons optimization algorithm，SOA）附matlab代码

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智能优化算法  神经网络预测 雷达通信  无线传感器

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⛄ 内容介绍

This paper introduces a new stochastic bio-inspired optimization algorithm, denoted as seasons optimization (SO) algorithm.This algorithm is inspired by the growth cycle of trees in diferent seasons of a year. It is an iterative and population-based algorithm working with a population of initial solutions known as a forest. Each individual in the forest is referred to as a tree. Until the termination conditions are satisfed, the trees in the forest are updated to a new generation by applying four operators similar to the trees’ life cycles in nature: renew, competition, seeding, and resistance. These operators hopefully cause the trees to converge towards the global optimum of the optimization problem. The efectiveness of the proposed SO algorithm is evaluated using multi-variable single-objective test problems and compared with several well-known baseline and state-of-the-art algorithms. The results show that the proposed algorithm outperformed its counterparts in terms of solution quality and fnding the global optimum on most benchmark functions.

⛄ 部分代码

clear all;

clc;

close all

%% Problem Statement

ProblemParams.CostFuncName = 'F4';

objFunc= 'F4';

[fobj, lowerbound, upperbound, globalCost, dimension]=GetBenchmarkFunction(ProblemParams.CostFuncName);

ProblemParams.CostFuncName=fobj;

ProblemParams.lb=lowerbound;

ProblemParams.ub=upperbound;

ProblemParams.NPar = dimension;

ProblemParams.gcost=globalCost;

ProblemParams.VarMin =ProblemParams.lb;

ProblemParams.VarMax = ProblemParams.ub;

if numel(ProblemParams.VarMin)==1

    ProblemParams.VarMin=repmat(ProblemParams.VarMin,1,ProblemParams.NPar);

    ProblemParams.VarMax=repmat(ProblemParams.VarMax,1,ProblemParams.NPar);

end

ProblemParams.SearchSpaceSize = ProblemParams.VarMax - ProblemParams.VarMin;

AlgorithmParams.NumOfTrees = 8;

AlgorithmParams.NumOfYears = 50;

AlgorithmParams.Pmin = 0.4;

AlgorithmParams.Pmax = 0.6;

%% Main Loop

for year= 1:AlgorithmParams.NumOfYears

    

    p=AlgorithmParams.Pmax-(year/AlgorithmParams.NumOfYears)*(AlgorithmParams.Pmax-AlgorithmParams.Pmin);            %pr, ps and pw are in the range [0.4, 0.6]

    AlgorithmParams.RenewRate=p;

    AlgorithmParams.SeedingRate=p;

    AlgorithmParams.ColdThreshold=p;

    AlgorithmParams.CompetitionRate = p;

    

    

    %% Spring Season

    if (year==1)

        InitialTrees = CreateForest(AlgorithmParams, ProblemParams);

        Forest=InitialTrees;

        InitialCost = feval(ProblemParams.CostFuncName,InitialTrees);

        Forest(:,end+1) = InitialCost;

    else

        Forest = Renew(Forest, Seeds, AlgorithmParams, ProblemParams);

    end

    

    

    %% Summer Season  (Growth & Competition)

    [Forest] = Competition (Forest, AlgorithmParams, ProblemParams, year);

    

    %% Autumn Season

    Seeds = Seeding(Forest,AlgorithmParams, ProblemParams);

    s=size(Seeds,1);

    AlgorithmParams.s=s;

    

    %% Winter Season

    Forest = Resistance(Forest,AlgorithmParams, ProblemParams);

    

    Costs = Forest(:,end);

    MinimumCost(year) = min(Costs);

    

    fprintf('Minimum Cost in Iteration %d is %3.16f \n', year,MinimumCost(year));

    

end  

figure;

subplot(121)

func_plot(objFunc);

title(objFunc)

xlabel('x_1');

ylabel('x_2');

zlabel([objFunc,'( x_1 , x_2 )'])

subplot(122)

semilogy(MinimumCost,'LineWidth',3);

xlabel('Iterations');

ylabel('Best fitness obtained so far');

legend('SOA');

box on;

axis tight;

grid off;

⛄ 运行结果

⛄ 参考文献

​Emami, Hojjat. “Seasons Optimization Algorithm.” Engineering with Computers, vol. 38, no. 2, Springer Science and Business Media LLC, Aug. 2020, pp. 1845–65, doi:10.1007/s00366-020-01133-5.

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