全局搜索策略的鲸鱼优化算法GSWOA助力SVM参数c和g优化，构建多维输入单维输出预测模型,基于全局搜索策略的鲸鱼优化算法GSWOA的SVM参数c和g寻优建立预测模型,一种全局搜索策略的鲸鱼优化算法G
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全局搜索策略的鲸鱼优化算法GSWOA助力SVM参数c和g优化，构建多维输入单维输出预测模型,基于全局搜索策略的鲸鱼优化算法GSWOA的SVM参数c和g寻优建立预测模型,一种全局搜索策略的鲸鱼优化算法GSWOA对SVM的参数c和g做寻优，优化两个最佳参数，然后建立多维输入单维输出的预测模型，具体预测效果如下图所示，代码内有注释，直接替数据就可以使用。,全局搜索策略; 鲸鱼优化算法GSWOA; SVM参数寻优; 参数c和g; 最佳参数优化; 多维输入单维输出预测模型; 代码注释替换。,全局搜索策略GSWOA优化SVM参数c和g，高效预测模型构建与效果展示 <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/90430923/2/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/90430923/bg1.jpg"/><div class="t m0 x1 h2 y1 ff1 fs0 fc0 sc0 ls0 ws0">探索鲸鱼优化算法<span class="_ _0"> </span><span class="ff2">GSWOA<span class="_ _0"> </span></span>在<span class="_ _0"> </span><span class="ff2">SVM<span class="_"> </span></span>参数寻优中的应用</div><div class="t m0 x1 h2 y2 ff2 fs0 fc0 sc0 ls0 ws0">======================</div><div class="t m0 x1 h2 y3 ff1 fs0 fc0 sc0 ls0 ws0">一、引言</div><div class="t m0 x1 h2 y4 ff2 fs0 fc0 sc0 ls0 ws0">----</div><div class="t m0 x1 h2 y5 ff1 fs0 fc0 sc0 ls0 ws0">在机器学习的领域中，<span class="_ _1"></span>支持向量机<span class="_ _1"></span>（<span class="ff2">SVM</span>）<span class="_ _1"></span>是一种广泛应用的分类和回归分析工具。<span class="_ _1"></span>其性能</div><div class="t m0 x1 h2 y6 ff1 fs0 fc0 sc0 ls0 ws0">在很大程度上依赖于两个关键参数<span class="_ _1"></span>：<span class="_ _1"></span>惩罚系数<span class="_ _0"> </span><span class="ff2">c<span class="_ _0"> </span></span>和核函数参数<span class="_ _0"> </span><span class="ff2">g</span>。如何有效地寻找这两个参</div><div class="t m0 x1 h2 y7 ff1 fs0 fc0 sc0 ls0 ws0">数<span class="_ _2"></span>的<span class="_ _2"></span>最<span class="_ _2"></span>佳<span class="_ _2"></span>值<span class="_ _2"></span>，<span class="_ _2"></span>一<span class="_ _2"></span>直<span class="_ _2"></span>是<span class="_ _2"></span>研<span class="_ _2"></span>究<span class="_ _2"></span>的<span class="_ _2"></span>热<span class="_ _2"></span>点<span class="_ _2"></span>。<span class="_ _2"></span>本<span class="_ _2"></span>文<span class="_ _2"></span>将<span class="_ _2"></span>介<span class="_ _2"></span>绍<span class="_ _2"></span>一<span class="_ _2"></span>种<span class="_ _2"></span>全<span class="_ _2"></span>局<span class="_ _2"></span>搜<span class="_ _2"></span>索<span class="_ _2"></span>策<span class="_ _2"></span>略<span class="_ _2"></span><span class="ff2">——</span>鲸<span class="_ _2"></span>鱼<span class="_ _2"></span>优<span class="_ _2"></span>化<span class="_ _2"></span>算<span class="_ _2"></span>法</div><div class="t m0 x1 h2 y8 ff1 fs0 fc0 sc0 ls0 ws0">（<span class="ff2">GSWOA</span>）<span class="_ _3"></span>，并探讨其如何对<span class="_ _0"> </span><span class="ff2">SVM<span class="_"> </span></span>的<span class="_ _0"> </span><span class="ff2">c<span class="_ _0"> </span></span>和<span class="_ _0"> </span><span class="ff2">g<span class="_ _0"> </span></span>参数进行寻优，并建立多维输入单维输出的预</div><div class="t m0 x1 h2 y9 ff1 fs0 fc0 sc0 ls0 ws0">测模型。</div><div class="t m0 x1 h2 ya ff1 fs0 fc0 sc0 ls0 ws0">二、鲸鱼优化算法（<span class="ff2">GSWOA</span>）简介</div><div class="t m0 x1 h2 yb ff2 fs0 fc0 sc0 ls0 ws0">------------</div><div class="t m0 x1 h2 yc ff1 fs0 fc0 sc0 ls0 ws0">鲸鱼优化算法是一种新型的全局优化算法，<span class="_ _4"></span>其灵感来源于鲸鱼的游动行为。<span class="_ _4"></span>通过模拟鲸鱼的</div><div class="t m0 x1 h2 yd ff1 fs0 fc0 sc0 ls0 ws0">社会行为和游动规律，<span class="_ _1"></span><span class="ff2">GSWOA<span class="_"> </span><span class="ff1">能够在搜索空间中高效地寻找全局最优解。<span class="_ _1"></span>这种算法的特点</span></span></div><div class="t m0 x1 h2 ye ff1 fs0 fc0 sc0 ls0 ws0">是能够处理复杂的非线性问题，并且在搜索过程中具有较强的鲁棒性。</div><div class="t m0 x1 h2 yf ff1 fs0 fc0 sc0 ls0 ws0">三、<span class="ff2">GSWOA<span class="_ _0"> </span></span>在<span class="_ _0"> </span><span class="ff2">SVM<span class="_"> </span></span>参数寻优中的应用</div><div class="t m0 x1 h2 y10 ff2 fs0 fc0 sc0 ls0 ws0">--------------</div><div class="t m0 x1 h2 y11 ff1 fs0 fc0 sc0 ls0 ws0">在<span class="_ _0"> </span><span class="ff2">SVM<span class="_"> </span></span>的参数寻优过程中，我们利用<span class="_ _0"> </span><span class="ff2">GSWOA<span class="_ _0"> </span></span>的全局搜索能力，对<span class="_ _0"> </span><span class="ff2">c<span class="_"> </span></span>和<span class="_ _0"> </span><span class="ff2">g<span class="_ _0"> </span></span>两个参数进行寻</div><div class="t m0 x1 h2 y12 ff1 fs0 fc0 sc0 ls0 ws0">优。通过<span class="_ _0"> </span><span class="ff2">GSWOA<span class="_"> </span></span>的迭代计算，我们可以找到使<span class="_ _0"> </span><span class="ff2">SVM<span class="_ _0"> </span></span>模型预测效果最佳的最佳<span class="_ _0"> </span><span class="ff2">c<span class="_"> </span></span>和<span class="_ _0"> </span><span class="ff2">g<span class="_ _0"> </span></span>值。</div><div class="t m0 x1 h2 y13 ff1 fs0 fc0 sc0 ls0 ws0">这一过程不仅提高了<span class="_ _0"> </span><span class="ff2">SVM<span class="_"> </span></span>的性能，还使得模型更加适应具体的应用场景。</div></div><div class="pi" data-data='{"ctm":[1.611830,0.000000,0.000000,1.611830,0.000000,0.000000]}'></div></div>