Mobile　Devices　(MDs)　support　various　delay-sensitive　and　computation-intensive　applications.　Yet　they　only　have　limited　battery　energy　and　computing　resources,　thereby　failing　to　totally　run　all　applications.　A　mobile　edge　computing　(MEC)　paradigm　has　been　proposed　to　provide　additional　computation,　storage,　and　networking　resources　for　MDs.　Servers　in　MEC　are　often　deployed　in　both　macro　base　stations　(MBSs)　and　small　base　stations　(SBSs).　Thus,　it　is　highly　challenging　to　associate　resource-limited　MDs　to　them　with　high　performance,　and　realize　partial　computation　offloading　among　them　for　minimizing　total　energy　consumption　of　an　MEC　system.　To　tackle　these　challenges,　this　work　proposes　a　novel　computation　offloading　approach　for　delay-sensitive　applications　with　multiple　separable　tasks　in　hybrid　networks　including　MDs,　SBSs,　and　an　MBS.　To　achieve　it,　this　work　formulates　total　energy　consumption　minimization　as　a　constrained　mixed　integer　non-linear　program.　To　solve　it,　this　work　designs　an　improved　meta-heuristic　optimization　algorithm　called　Particle　swarm　optimization　based　on　Genetic　Learning　(PGL),　which　integrates　strong　local　search　capacity　of　a　particle　swarm　optimizer,　and　genetic　operations　of　a　genetic　algorithm.　PGL　jointly　optimizes　task　offloading　among　MDs,　SBSs,　and　MBS,　users＇　connection　to　SBSs,　MDs＇　CPU　speeds　and　transmission　power,　SBSs　and　MBS,　and　bandwidth　allocation　of　available　channels.　Simulations　with　real-world　data　collected　from　Google　cluster　trace　demonstrate　that　PGL　significantly　outperforms　other　existing　methods　in　total　energy　consumption　of　an　entire　system.