Pareto　Local　Search　(PLS)　is　a　simple,　yet　effective　optimization　approach　dedicated　to　multi-objective　combinatorial　optimization.　It　can　however　suffer　from　a　high　computational　cost,　especially　when　the　size　of　the　Pareto　optimal　set　is　relatively　large.　Recently,　incorporating　decomposition　in　PLS　had　revealed　a　high　potential,　not　only　in　providing　high-quality　approximation　sets,　but　also　in　speeding-up　the　search　process.　Using　the　bi-objective　Unconstrained　Binary　Quadratic　Programming　(bUBQP)　problem　as　an　illustrative　benchmark,　we　demonstrate　some　shortcomings　in　the　resulting　decomposition-guided　Parallel　Pareto　Local　Search　(PPLS),　and　we　propose　to　revisit　the　PPLS　design　accordingly.　For　instances　with　a　priori　unknown　Pareto　front　shape,　we　show　that　a　simple　pre-processing　technique　to　estimate　the　scale　of　the　Pareto　front　can　help　PPLS　to　better　balance　the　workload.　Furthermore,　we　propose　a　simple　technique　to　deal　with　the　critically-important　scalability　issue　raised　by　PPLS　when　deployed　over　a　large　number　of　computing　nodes.　Our　investigations　show　that　the　revisited　version　of　PPLS　provides　a　consistent　performance,　suggesting　that　decomposition-guided　PPLS　can　be　further　generalized　in　order　to　improve　both　parallel　efficiency　and　approximation　quality.　©　2018　Association　for　Computing　Machinery.