A suitable data structure to implement a priority queue is a binary heap. A binary heap is a binary tree-based data structure that satisfies the heap property, where each parent node has a value greater than or equal to its child nodes (in a max heap) or less than or equal to its child nodes (in a min heap).

Here's an outline of the data structure and algorithm to implement a priority queue using a binary heap:

1. Data Structure:

   • Use an array-based representation to store the elements of the priority queue.
   • Each element in the array represents a node in the binary heap.
   • The first element of the array (index 0) is left empty for easier calculations.
   • Maintain a variable to track the current size of the priority queue.

2. Operations: a) Insertion:

   • Add the new element at the end of the array.
   • Percolate the element up the heap by comparing it with its parent node and swapping if necessary to maintain the heap property.
   • Update the size of the priority queue.

   b) Deletion:

   • Remove the root element (highest priority) from the array.
   • Move the last element in the array to the root position.
   • Percolate the element down the heap by comparing it with its child nodes and swapping if necessary to maintain the heap property.
   • Update the size of the priority queue.

   c) Peek:

   • Return the value of the root element without removing it.

3. Algorithm Complexity:

   • Insertion: O(log n)
   • Deletion: O(log n)
   • Peek: O(1)

Using a binary heap-based implementation allows efficient insertion and deletion of elements while maintaining the desired priority order.

It's important to note that the above outline provides a basic framework, and there are variations and optimizations possible based on specific requirements. For example, you can implement a min heap or a max heap depending on the desired priority order, and additional operations like changing the priority of an element can be added as well.