diff --git a/analysis/engine/optimizer.py b/analysis/engine/optimizer.py
index 82ab43f60d69fc57f361088fe1141308c225df2d..4442472ebdf08c42fc756a5c8b6113d15a38d84c 100644
--- a/analysis/engine/optimizer.py
+++ b/analysis/engine/optimizer.py
@@ -69,7 +69,8 @@ class Optimizer(Resource):
                                      noise=args.get("noise"),
                                      sel_feature=args.get("feature_filter") or \
                                                  args.get("sel_feature"),
-                                     feature_selector=args.get("feature_selector"))
+                                     feature_selector=args.get("feature_selector"),
+                                     history_path=args.get("history_path"))
         engine.start()
 
         value = {}
diff --git a/analysis/engine/parser.py b/analysis/engine/parser.py
index c36c74d8a259363c53004967ac59d1d7b8b58c6d..2e9c7ac3a269c900e2a367fd289f065eeaba6346 100644
--- a/analysis/engine/parser.py
+++ b/analysis/engine/parser.py
@@ -25,8 +25,10 @@ OPTIMIZER_POST_PARSER.add_argument('knobs', type=list, location='json',
 OPTIMIZER_POST_PARSER.add_argument('engine',
                                    choices=('random', 'forest', 'gbrt', 'extraTrees',
                                             'bayes', 'abtest', 'lhs', 'tpe', 'gridsearch',
-                                            'traverse', 'ppo'),
+                                            'traverse', 'ppo', 'Bayesian_Transfer'),
                                    help='engine choice: {error_msg}')
+OPTIMIZER_POST_PARSER.add_argument('history_path', type=list,location='json',
+                                   help='path to load tuning history')
 OPTIMIZER_POST_PARSER.add_argument('random_starts', type=int, location='json',
                                    help="random_starts cannot be null")
 OPTIMIZER_POST_PARSER.add_argument('x_ref', type=list, location='json',
diff --git a/analysis/optimizer/optimizer.py b/analysis/optimizer/optimizer.py
index 1f026328d82f52d5419ea739d349d1cdbef4a2ed..e37d97dbf03f803d6394e19e1b7e441b181e5b6b 100755
--- a/analysis/optimizer/optimizer.py
+++ b/analysis/optimizer/optimizer.py
@@ -33,7 +33,7 @@ from analysis.optimizer.abtest_tuning_manager import ABtestTuningManager
 from analysis.optimizer.gridsearch_tuning_manager import GridSearchTuningManager
 from analysis.optimizer.weighted_ensemble_feature_selector import WeightedEnsembleFeatureSelector
 from analysis.optimizer.variance_reduction_feature_selector import VarianceReductionFeatureSelector
-
+from analysis.optimizer.BO_tuning_manager import BO_Optimizer
 
 LOGGER = logging.getLogger(__name__)
 
@@ -43,7 +43,7 @@ class Optimizer(multiprocessing.Process):
 
     def __init__(self, name, params, child_conn, prj_name, engine="bayes",
                  max_eval=50, sel_feature=False, x0=None, y0=None,
-                 n_random_starts=20, split_count=5, noise=0.00001 ** 2, feature_selector="wefs"):
+                 n_random_starts=20, split_count=5, noise=0.00001 ** 2, feature_selector="wefs", history_path=None):
         super().__init__(name=name)
         self.knobs = self.check_multiple_params(params)
         self.child_conn = child_conn
@@ -62,6 +62,10 @@ class Optimizer(multiprocessing.Process):
             self.ref = []
         self._n_random_starts = 20 if n_random_starts is None else n_random_starts
         self.feature_selector = feature_selector
+        if history_path is None:
+            self.history_path = []
+        else:
+            self.history_path = history_path
 
     def build_space(self):
         """build space"""
@@ -322,7 +326,7 @@ class Optimizer(multiprocessing.Process):
                 options = gstuning_manager.get_options_index(options)
             elif self.engine == 'GA':
                 from analysis.optimizer.gatuning_manager import GATuning
-                gatuning_manager = GATuning(self.knobs,self.child_conn)
+                gatuning_manager = GATuning(self.knobs, self.child_conn)
                 params = gatuning_manager.GA()
             elif self.engine == 'lhs':
                 from analysis.optimizer.knob_sampling_manager import KnobSamplingManager
@@ -357,12 +361,20 @@ class Optimizer(multiprocessing.Process):
             elif self.engine == 'ppo':
                 from analysis.optimizer.ppotuning_manager import PPOOptimizer
                 ppo_opt = PPOOptimizer(
-                        self.knobs, self.child_conn, self.max_eval)
+                    self.knobs, self.child_conn, self.max_eval)
                 best_params = ppo_opt.run()
                 final_param = dict(finished=True, param=best_params)
                 self.child_conn.send(final_param)
                 return best_params
 
+            elif self.engine == 'Bayesian_Transfer':
+                bo_opt = BO_Optimizer(self.knobs, self.child_conn, self.max_eval,
+                                      prj_name=self.project_name + "_tlbo_" + str(int(round(time.time() * 1000))),
+                                      history_path=self.history_path)
+                best_params = bo_opt.run()
+                final_param = dict(finished=True, param=best_params)
+                self.child_conn.send(final_param)
+
             LOGGER.info("Minimization procedure has been completed.")
         except ValueError as value_error:
             LOGGER.error('Value Error: %s', repr(value_error))