【摘要】：針對傳統(tǒng)進(jìn)化算法在求解多目標(biāo)優(yōu)化問題時(shí)存在多樣性和收斂性不佳的問題,提出一種外部種群完全反饋的元胞差分算法。對標(biāo)準(zhǔn)元胞差分算法進(jìn)行改進(jìn),在每一代進(jìn)化之后,根據(jù)秩與k最近鄰距離對外部種群進(jìn)行修剪,并將修剪后的整個(gè)外部種群隨機(jī)分配到二維網(wǎng)狀結(jié)構(gòu),在原有變異操作中引入新的擾動來避免算法陷入局部最優(yōu)。通過對6個(gè)基準(zhǔn)函數(shù)進(jìn)行測試表明,新算法相對于其他3種典型算法具有更好的前端覆蓋性,新的變異方式能提高算法跳出局部最優(yōu)解的能力。通過工程實(shí)例驗(yàn)證了所提算法的可行性與有效性。
[Abstract]:Aiming at the diversity and poor convergence of traditional evolutionary algorithm in solving multi-objective optimization problems, a cellular difference algorithm with complete feedback of external population is proposed. The standard cellular difference algorithm is improved. After each generation evolves, the external population is pruned according to the distance between rank and k nearest neighbor, and the whole external population is randomly assigned to the two-dimensional mesh structure. A new disturbance is introduced into the original mutation operation to avoid the algorithm falling into local optimum. The test of six benchmark functions shows that the new algorithm has better front-end coverage than the other three typical algorithms, and the new mutation method can improve the ability of the algorithm to jump out of the local optimal solution. The feasibility and effectiveness of the proposed algorithm are verified by an engineering example.
【作者單位】： 浙江工業(yè)大學(xué)機(jī)械工程學(xué)院;
【基金】：浙江省自然科學(xué)基金資助項(xiàng)目(LY16G010013) 國家自然科學(xué)基金資助項(xiàng)目(71371170,71301148) 國家863計(jì)劃資助項(xiàng)目(2015AA043002)~~
【分類號】：TP18

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