Query optimization is challenging task in database. Many different types of techniques used to optimize query. Heuristic Greedy, Iterative Improvement and Ant Colony algorithms is being used to query optimization. Ant colony Algorithm used to find optimal solution for different type of problems. In this paper we modify Ant Colony Algorithm for query optimization and will show the comparison execution time between Heuristic based optimization, Ant Colony Optimization and Modified Ant Colony optimization algorithms. After implementation of said existing algorithms and modified Ant Colony optimization algorithms we found that modified Ant colony taking less computation time as compare to others algorithms.

Query optimization is the overall process of choosing the most efficient means of executing a SQL statement. The optimizer attempts to generate the best execution plan for a SQL statement. The best execution plan is defined as the plan with the lowest cost among all considered candidate plans. SQL is a nonprocedural language, so the optimizer is free to merge, reorganize, and process in any order. The cost is a number that represents the estimated resource usage for an execution plan. The cost computation accounts for factors of query execution such as I/O, CPU, and communication. To implement query optimization methods such as Heuristic Greedy based optimization, Iterative Improvement based cost optimization and Ant Colony optimization algorithms. Show Comparison of cost, execution time and response time between Heuristic Greedy based optimization; Ant Colony Optimization and Iterative Improvement based cost optimization algorithms.