Recently,　deep　neural　networks　(DNNs)　have　become　the　mainstream　strategy　for　speech　enhancement　task　because　it　can　achieve　the　higher　speech　quality　and　intelligibility　than　the　traditional　methods.　However,　these　DNN-based　methods　always　need　a　large　number　of　parallel　corpus　consisting　of　clean　speech　and　noise　to　produce　noisy　data　for　the　training　of　the　DNN　in　order　to　improve　the　generalization　of　the　network.　As　a　result,　this　implies　that　many　noisy　speech　signals　that　are　collected　in　real　environment　cannot　be　used　to　train　the　DNN　because　of　the　lack　of　corresponding　clean　speech　and　noise.　Additionally,　as　we　know,　noise　varies　with　the　time　and　scenario,　so　we　cannot　obtain　parallel　speech　and　noise　due　to　infinite　noise　data　and　some　limited　speech　data.　Thus,　the　network　training　with　unparallel　speech　and　noise　data　is　essential　for　the　generalization　of　the　network.　To　address　this　problem,　we　propose　a　novel　parallel-data-free　speech　enhancement　method,　in　which　the　cycle-consistent　generative　adversarial　network　(CycleGAN)　and　multi-objective　learning　are　employed.　Our　method　is　also　able　to　make　best　use　of　the　benefits　of　multi-objective　learning.　On　the　training　stage,　we　utilize　two　different　encoders　to　encode　the　features　of　clean　speech　and　noisy　speech,　respectively.　Then,　two　forward　generators　are　immediately　used　to　predict　the　ideal　time-frequency　(T-F)　mask　and　log-power　spectrum　(LPS)　of　clean　speech.　Two　inverse　generators　are　applied　to　map　the　magnitude　spectrum　(MS)　and　LPS　of　noisy　speech,　respectively.　In　addition,　four　discriminators　are　used　to　distinguish　the　real　speech　features　from　the　generated　features.　Two　encoders,　four　generators　and　four　discriminators　are　simultaneously　trained　by　using　adversarial,　identity-mapping,　latent　similarity　and　cycle-consistent　loss.　On　the　test　stage,　we　directly　utilize　the　forward　generators　and　encoders　to　acquire　the　enhanced　speech.　The　experimental　results　indicate　that　the　proposed　approach　is　able　to　achieve　the　better　speech　enhancement　performance　than　the　reference　methods.　Moreover,　the　proposed　method　is　also　effective　to　improve　speech　quality　and　intelligibility　when　the　networks　are　trained　under　the　parallel　data.