網(wǎng)站建設(shè)怎么做分錄,查看網(wǎng)站注冊信息,網(wǎng)站用哪種語言,網(wǎng)站群系統(tǒng)建設(shè)思路? 博主簡介#xff1a;擅長數(shù)據(jù)搜集與處理、建模仿真、程序設(shè)計、仿真代碼、論文寫作與指導(dǎo)#xff0c;畢業(yè)論文、期刊論文經(jīng)驗交流。? 具體問題可以私信或掃描文章底部二維碼。1) 針對高維全局優(yōu)化問題中生物地理學(xué)優(yōu)化算法易陷入局部最優(yōu)、收斂速度慢的不足#xff0c;提…?博主簡介擅長數(shù)據(jù)搜集與處理、建模仿真、程序設(shè)計、仿真代碼、論文寫作與指導(dǎo)畢業(yè)論文、期刊論文經(jīng)驗交流。? 具體問題可以私信或掃描文章底部二維碼。1) 針對高維全局優(yōu)化問題中生物地理學(xué)優(yōu)化算法易陷入局部最優(yōu)、收斂速度慢的不足提出一種融合正弦余弦算法和動態(tài)混合變異的對偶BBO變體SCBBO。該算法首先采用拉丁超立方抽樣初始化種群確保解空間均勻覆蓋其次在遷移算子中嵌入改進(jìn)的正弦余弦更新公式引入非線性遞減參數(shù)和慣性權(quán)重增強(qiáng)高維環(huán)境下的勘探能力然后設(shè)計動態(tài)混合變異算子結(jié)合拉普拉斯變異的大步長探索和高斯變異的精細(xì)開發(fā)根據(jù)迭代進(jìn)度自適應(yīng)調(diào)整變異概率平衡全局與局部搜索最后集成對偶學(xué)習(xí)策略為每個個體生成對稱解通過比較選擇更優(yōu)者提升收斂精度。理論分析通過構(gòu)造數(shù)列收斂模型證明算法以概率1全局收斂。在CEC2013高維測試集上的實驗表明SCBBO在1000維至10000維問題上均保持穩(wěn)定性能顯著優(yōu)于對比算法。(2) 為解決復(fù)雜多峰優(yōu)化問題中BBO算法開發(fā)能力弱、參數(shù)敏感的問題提出一種基于混合遷移算子和反饋差分進(jìn)化機(jī)制的BBO變體HFBBO。該算法引入榜樣學(xué)習(xí)法在遷移過程中禁止劣解覆蓋優(yōu)解保留精英信息混合遷移算子通過隨機(jī)切換全局遷移向最優(yōu)個體學(xué)習(xí)和局部遷移向鄰域個體學(xué)習(xí)實現(xiàn)搜索模式動態(tài)調(diào)整反饋差分進(jìn)化機(jī)制替代傳統(tǒng)變異根據(jù)種群多樣性指標(biāo)自動選擇差分變異策略如DE/rand/1或DE/best/2避免早熟收斂。算法收斂性通過Markov鏈模型證明復(fù)雜度分析顯示其與標(biāo)準(zhǔn)BBO相當(dāng)。在CEC2014和CEC2017測試集的廣泛實驗中HFBBO在多數(shù)函數(shù)上獲得最優(yōu)結(jié)果Friedman檢驗排名第一顯示出強(qiáng)大魯棒性。(3) 面向現(xiàn)實工程中的約束優(yōu)化問題將改進(jìn)的Oracle懲罰函數(shù)法與BBO算法結(jié)合處理等式和不等式約束。該方法將約束違反度轉(zhuǎn)化為自適應(yīng)懲罰項動態(tài)調(diào)整懲罰系數(shù)確保搜索朝向可行域同時針對混合變量問題設(shè)計通用離散化處理模塊將離散變量映射為連續(xù)編碼優(yōu)化后再逆映射回原始空間。基于此開發(fā)SCBBO-CH和HFBBO-CH兩種算法應(yīng)用于CEC2020現(xiàn)實約束優(yōu)化測試集的57個問題涵蓋機(jī)械設(shè)計、資源分配等領(lǐng)域。實驗結(jié)果表明所提算法在近半數(shù)問題上達(dá)到理論最優(yōu)解且求解效率高于傳統(tǒng)罰函數(shù)法驗證了其在復(fù)雜約束處理中的有效性。import numpy as np import math def latin_hypercube_sampling(num, dim, bounds): samples np.zeros((num, dim)) for i in range(dim): segment (bounds[1] - bounds[0]) / num samples[:, i] np.array([bounds[0] (j np.random.rand()) * segment for j in range(num)]) np.random.shuffle(samples[:, i]) return samples class SCBBO: def __init__(self, num_species, dim, bounds, objective): self.population latin_hypercube_sampling(num_species, dim, bounds) self.fitness np.array([objective(ind) for ind in self.population]) self.bounds bounds self.dim dim self.best_idx np.argmin(self.fitness) self.best_solution self.population[self.best_idx].copy() self.best_fitness self.fitness[self.best_idx] def migration(self, migration_rates): new_pop self.population.copy() for i in range(len(self.population)): if np.random.rand() migration_rates[i]: j np.random.randint(len(self.population)) for k in range(self.dim): if np.random.rand() 0.5: new_pop[i][k] self.population[j][k] return new_pop def sca_update(self, iteration, max_iter): a 2 - iteration * (2 / max_iter) new_pop self.population.copy() for i in range(len(self.population)): r1 a * (1 - iteration / max_iter) r2 2 * math.pi * np.random.rand() r3 2 * np.random.rand() r4 np.random.rand() if r4 0.5: new_pop[i] r1 * math.sin(r2) * abs(r3 * self.best_solution - self.population[i]) else: new_pop[i] r1 * math.cos(r2) * abs(r3 * self.best_solution - self.population[i]) return new_pop def dynamic_mutation(self, iteration, max_iter): mutated self.population.copy() mutation_prob 0.1 * (1 - iteration / max_iter) for i in range(len(self.population)): if np.random.rand() mutation_prob: if np.random.rand() 0.5: mutated[i] np.random.laplace(0, 1, self.dim) * 0.5 else: mutated[i] np.random.normal(0, 1, self.dim) * 0.2 return mutated def dual_learning(self): dual_pop -self.population.copy() return np.clip(dual_pop, self.bounds[0], self.bounds[1]) def optimize(self, max_iter, objective): for iter in range(max_iter): migration_rates 1 - (self.fitness - np.min(self.fitness)) / (np.max(self.fitness) - np.min(self.fitness) 1e-8) self.population self.migration(migration_rates) self.population self.sca_update(iter, max_iter) self.population self.dynamic_mutation(iter, max_iter) dual_pop self.dual_learning() combined_pop np.vstack([self.population, dual_pop]) combined_fitness np.array([objective(ind) for ind in combined_pop]) best_combined_idx np.argsort(combined_fitness)[:len(self.population)] self.population combined_pop[best_combined_idx] self.fitness combined_fitness[best_combined_idx] self.best_idx np.argmin(self.fitness) if self.fitness[self.best_idx] self.best_fitness: self.best_fitness self.fitness[self.best_idx] self.best_solution self.population[self.best_idx].copy() return self.best_solution, self.best_fitness def ackley_function(x): return -20 * np.exp(-0.2 * np.sqrt(np.mean(x ** 2))) - np.exp(np.mean(np.cos(2 * math.pi * x))) 20 math.e scbbo SCBBO(50, 10, [-32, 32], ackley_function) best_sol, best_fit scbbo.optimize(100, ackley_function) print(fBest Solution: {best_sol}, Best Fitness: {best_fit})如有問題可以直接溝通