2021年最新改进天鹰优化算法：引入细菌增长模型等多策略提升性能,基于改进天鹰优化算法(IAO)的细菌增长模型与多策略融合优化器,改进天鹰优化算法(IAO)，天鹰优化器，2021年较新优化算法，性能非
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2021年最新改进天鹰优化算法：引入细菌增长模型等多策略提升性能,基于改进天鹰优化算法(IAO)的细菌增长模型与多策略融合优化器,改进天鹰优化算法(IAO)，天鹰优化器，2021年较新优化算法，性能非常好。引入细菌增长模型等策略，进行改进对比算法：沙丘猫优化算法，海鸥优化算法，鲸鱼优化算法，天鹰优化器，飞蛾扑火算法。初始种群:30 独立运行次数：30 最大迭代次数：5003种改进策略，有参考。23种基准测试函数，有函数表达式，具体效果图如下。,核心关键词：IAO（改进天鹰优化算法）；天鹰优化器；优化算法；性能优越；细菌增长模型；改进策略；对比算法；沙丘猫优化等；海鸥优化算法；鲸鱼优化算法；飞蛾扑火算法；初始种群；独立运行次数；最大迭代次数；基准测试函数；函数表达式；效果图。,改进天鹰优化算法：引入细菌增长模型与多策略优化提升性能 <link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/css/base.min.css" rel="stylesheet"/><link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/css/fancy.min.css" rel="stylesheet"/><link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/90402924/2/raw.css" rel="stylesheet"/><div id="sidebar" style="display: none"><div id="outline"></div></div><div class="pf w0 h0" data-page-no="1" id="pf1"><div class="pc pc1 w0 h0"><img alt="" class="bi x0 y0 w1 h1" src="/image.php?url=https://csdnimg.cn/release/download_crawler_static/90402924/bg1.jpg"/><div class="t m0 x1 h2 y1 ff1 fs0 fc0 sc0 ls0 ws0">**<span class="ff2">改进天鹰优化算法</span>(IAO)<span class="ff2">及其与多种优化算法的对比研究</span>**</div><div class="t m0 x1 h2 y2 ff2 fs0 fc0 sc0 ls0 ws0">一<span class="ff3">、</span>引言</div><div class="t m0 x1 h2 y3 ff2 fs0 fc0 sc0 ls0 ws0">在优化算法的研究领域中<span class="ff4">，</span>天鹰优化器<span class="ff1">(Eagle Optimization Algorithm, EOA)</span>作为近年来的</div><div class="t m0 x1 h2 y4 ff2 fs0 fc0 sc0 ls0 ws0">重要研究成果<span class="ff4">，</span>以其出色的性能和良好的全局搜索能力引起了广泛关注<span class="ff3">。</span>然而<span class="ff4">，</span>任何算法都有其改进</div><div class="t m0 x1 h2 y5 ff2 fs0 fc0 sc0 ls0 ws0">的空间<span class="ff3">。</span>在<span class="_ _0"> </span><span class="ff1">2021<span class="_ _1"> </span></span>年<span class="ff4">，</span>为了进一步提高天鹰优化器的性能<span class="ff4">，</span>我们引入了细菌增长模型等策略<span class="ff4">，</span>提出了</div><div class="t m0 x1 h2 y6 ff2 fs0 fc0 sc0 ls0 ws0">改进天鹰优化算法<span class="ff1">(Improved Eagle Optimization Algorithm, IAO)<span class="ff3">。</span></span>本文将详细介绍<span class="_ _0"> </span><span class="ff1">IAO</span></div><div class="t m0 x1 h2 y7 ff2 fs0 fc0 sc0 ls0 ws0">算法的原理<span class="ff3">、</span>实现及与多种优化算法的对比研究<span class="ff3">。</span></div><div class="t m0 x1 h2 y8 ff2 fs0 fc0 sc0 ls0 ws0">二<span class="ff3">、<span class="ff1">IAO<span class="_ _1"> </span></span></span>算法的改进策略</div><div class="t m0 x1 h2 y9 ff1 fs0 fc0 sc0 ls0 ws0">1.<span class="_ _2"> </span><span class="ff2">引入细菌增长模型<span class="ff4">：</span>细菌增长模型是一种自然界的生物增长模型<span class="ff4">，</span>其增长策略可以借鉴到优化算</span></div><div class="t m0 x2 h2 ya ff2 fs0 fc0 sc0 ls0 ws0">法中<span class="ff3">。</span>我们通过将细菌增长模型与天鹰优化器相结合<span class="ff4">，</span>增强了算法的局部搜索能力和收敛速度<span class="ff3">。</span></div><div class="t m0 x1 h2 yb ff1 fs0 fc0 sc0 ls0 ws0">2.<span class="_ _2"> </span><span class="ff2">多样化搜索策略<span class="ff4">：</span>在搜索过程中<span class="ff4">，</span>通过引入随机性<span class="ff4">，</span>使算法在搜索空间中能够更加灵活地寻找最</span></div><div class="t m0 x2 h2 yc ff2 fs0 fc0 sc0 ls0 ws0">优解<span class="ff3">。</span></div><div class="t m0 x1 h2 yd ff1 fs0 fc0 sc0 ls0 ws0">3.<span class="_ _2"> </span><span class="ff2">动态调整种群规模<span class="ff4">：</span>根据搜索进程的进展<span class="ff4">，</span>动态地调整初始种群规模<span class="ff4">，</span>以提高算法的效率和准确</span></div><div class="t m0 x2 h2 ye ff2 fs0 fc0 sc0 ls0 ws0">性<span class="ff3">。</span></div><div class="t m0 x1 h2 yf ff2 fs0 fc0 sc0 ls0 ws0">三<span class="ff3">、<span class="ff1">IAO<span class="_ _1"> </span></span></span>算法与其它优化算法的对比</div><div class="t m0 x1 h2 y10 ff2 fs0 fc0 sc0 ls0 ws0">为了验证<span class="_ _0"> </span><span class="ff1">IAO<span class="_ _1"> </span></span>算法的性能<span class="ff4">，</span>我们选择了沙丘猫优化算法<span class="ff3">、</span>海鸥优化算法<span class="ff3">、</span>鲸鱼优化算法以及原始的天</div><div class="t m0 x1 h2 y11 ff2 fs0 fc0 sc0 ls0 ws0">鹰优化器和飞蛾扑火算法进行对比<span class="ff3">。</span>实验参数设置为<span class="ff4">：</span>初始种群<span class="_ _0"> </span><span class="ff1">30<span class="ff4">，</span></span>独立运行次数<span class="_ _0"> </span><span class="ff1">30<span class="ff4">，</span></span>最大迭代次</div><div class="t m0 x1 h2 y12 ff2 fs0 fc0 sc0 ls0 ws0">数<span class="_ _0"> </span><span class="ff1">500<span class="ff3">。</span></span></div><div class="t m0 x1 h2 y13 ff2 fs0 fc0 sc0 ls0 ws0">通过在<span class="_ _0"> </span><span class="ff1">23<span class="_ _1"> </span></span>种基准测试函数上的实验对比<span class="ff4">，</span>我们发现<span class="_ _0"> </span><span class="ff1">IAO<span class="_ _1"> </span></span>算法在大多数情况下都表现出了优越的性能</div><div class="t m0 x1 h2 y14 ff3 fs0 fc0 sc0 ls0 ws0">。<span class="ff2">具体来说<span class="ff4">，<span class="ff1">IAO<span class="_ _1"> </span></span></span>算法在收敛速度</span>、<span class="ff2">寻优精度以及稳定性方面都有显著提升</span>。</div><div class="t m0 x1 h2 y15 ff2 fs0 fc0 sc0 ls0 ws0">四<span class="ff3">、</span>实验结果与分析</div><div class="t m0 x1 h2 y16 ff1 fs0 fc0 sc0 ls0 ws0">1.<span class="_ _2"> </span><span class="ff2">收敛速度<span class="ff4">：</span></span>IAO<span class="_ _1"> </span><span class="ff2">算法在大多数测试函数上表现出更快的收敛速度<span class="ff4">，</span>能够在较少的迭代次数内找到</span></div><div class="t m0 x2 h2 y17 ff2 fs0 fc0 sc0 ls0 ws0">较优解<span class="ff3">。</span></div><div class="t m0 x1 h2 y18 ff1 fs0 fc0 sc0 ls0 ws0">2.<span class="_ _2"> </span><span class="ff2">寻优精度<span class="ff4">：</span></span>IAO<span class="_ _1"> </span><span class="ff2">算法的寻优精度高<span class="ff4">，</span>能够在搜索空间中找到更精确的最优解<span class="ff3">。</span></span></div><div class="t m0 x1 h2 y19 ff1 fs0 fc0 sc0 ls0 ws0">3.<span class="_ _2"> </span><span class="ff2">稳定性<span class="ff4">：</span></span>IAO<span class="_ _1"> </span><span class="ff2">算法的稳定性好<span class="ff4">，</span>多次独立运行的结果一致性高<span class="ff3">。</span></span></div><div class="t m0 x1 h2 y1a ff2 fs0 fc0 sc0 ls0 ws0">与其它对比算法相比<span class="ff4">，<span class="ff1">IAO<span class="_ _1"> </span></span></span>算法在性能上表现出较大的优势<span class="ff3">。</span>尤其是引入细菌增长模型等策略后<span class="ff4">，</span>算</div><div class="t m0 x1 h2 y1b ff2 fs0 fc0 sc0 ls0 ws0">法的搜索能力和局部开发能力得到了进一步提升<span class="ff3">。</span></div><div class="t m0 x1 h2 y1c ff2 fs0 fc0 sc0 ls0 ws0">五<span class="ff3">、</span>结论</div></div><div class="pi" data-data='{"ctm":[1.568627,0.000000,0.000000,1.568627,0.000000,0.000000]}'></div></div>