diff --git a/colossalai/auto_parallel/solver/op_handler/unary_elementwise_handler.py b/colossalai/auto_parallel/solver/op_handler/unary_elementwise_handler.py
index c6fbc3d2c..205923fa0 100644
--- a/colossalai/auto_parallel/solver/op_handler/unary_elementwise_handler.py
+++ b/colossalai/auto_parallel/solver/op_handler/unary_elementwise_handler.py
@@ -48,22 +48,23 @@ class UnaryElementwiseHandler(OperatorHandler):
         # For element-wise function, we keep the sharding spec of output node same as
         # the input. Therefore, the different strategies of input node with same
         # output sharding spec will generate same strategy for element-wise function.
-        sharding_spec_checklist = []
-        for strategy in self.input_node.strategies_vector:
+
+        for index, strategy in enumerate(self.input_node.strategies_vector):
             # It looks a little bit confusing, the input of the processing node
             # is the output of the input_node.
             input_sharding_spec = strategy.output_sharding_spec
             assert isinstance(input_sharding_spec, ShardingSpec), f'The input node should NOT be a tuple of tensor.'
-            if input_sharding_spec in sharding_spec_checklist:
-                continue
-            sharding_spec_checklist.append(input_sharding_spec)
+
             dim_partition_dict = deepcopy(input_sharding_spec.dim_partition_dict)
             try:
                 output_sharding_spec = self._generate_sharding_spec(self.output_data, dim_partition_dict)
             except AssertionError as e:
                 warnings.warn(f'{e}')
                 continue
-            name = f'{input_sharding_spec.sharding_sequence} -> {output_sharding_spec.sharding_sequence}'
+            # add index into name to pass the duplicated check
+            # we keep same strategies with different name for node merging, and it will not increase the searching space,
+            # because in solver, this node will be merged into other nodes, and solver will not create a new variable for this node.
+            name = f'{input_sharding_spec.sharding_sequence} -> {output_sharding_spec.sharding_sequence}_{index}'
             # TODO: use meta_info_prop to profile memory cost and compute cost
             compute_cost = self.output_data.numel()
             memory_cost = 0
diff --git a/tests/test_auto_parallel/test_strategies_constructor.py b/tests/test_auto_parallel/test_strategies_constructor.py
index ce263829a..ef8dbde03 100644
--- a/tests/test_auto_parallel/test_strategies_constructor.py
+++ b/tests/test_auto_parallel/test_strategies_constructor.py
@@ -59,7 +59,7 @@ def test_strategies_constructor():
     assert strategies_constructor.leaf_strategies[0][0].name == 'Replica Placeholder'
 
     # Second node is mul which is a element-wise node, therefore the output sharding spec is same as input sharding spec.
-    assert strategies_constructor.leaf_strategies[1][0].name == '[R, R, R, R] -> [R, R, R, R]'
+    assert strategies_constructor.leaf_strategies[1][0].name == '[R, R, R, R] -> [R, R, R, R]_0'
 
     # Third node is conv.
     conv_check_list = deepcopy(CONV_STRATEGIES_LIST)
@@ -79,7 +79,7 @@ def test_strategies_constructor():
 
     # Second node is mul which is a element-wise node, therefore the output sharding spec is same as input sharding spec.
     mul = nodes[1]
-    assert strategies_constructor.strategy_map[mul][0].name == '[R, R, R, R] -> [R, R, R, R]'
+    assert strategies_constructor.strategy_map[mul][0].name == '[R, R, R, R] -> [R, R, R, R]_0'
 
     # Third node is conv.
     conv = nodes[2]