Multi-Objective Ant Lion Optimizer for Solving Environmental/Economic Dispatch

2021 ◽  
Vol 1 (2) ◽  
pp. 155-160
Author(s):  
Hardiansyah HARDIANSYAH
Keyword(s):  
Environmental Economic ◽  
Economic Dispatch ◽  
Multi Objective ◽  
Ant Lion Optimizer ◽  
Ant Lion

Query Optimization in Crowd-Sourcing Using Multi-Objective Ant Lion Optimizer

2019 ◽  
Vol 14 (4) ◽  
pp. 50-63 ◽  
Author(s):  
Deepak Kumar ◽  
Deepti Mehrotra ◽  
Rohit Bansal
Keyword(s):  
Query Optimization ◽  
Query Language ◽  
Database Systems ◽  
Amazon Mechanical Turk ◽  
Optimization Approach ◽  
Crowd Sourcing ◽  
Market Place ◽  
Multi Objective ◽  
Ant Lion Optimizer ◽  
Ant Lion

Nowadays, query optimization is a biggest concern for crowd-sourcing systems, which are developed for relieving the user burden of dealing with the crowd. Initially, a user needs to submit a structured query language (SQL) based query and the system takes the responsibility of query compiling, generating an execution plan, and evaluating the crowd-sourcing market place. The input queries have several alternative execution plans and the difference in crowd-sourcing cost between the worst and best plans. In relational database systems, query optimization is essential for crowd-sourcing systems, which provides declarative query interfaces. Here, a multi-objective query optimization approach using an ant-lion optimizer was employed for declarative crowd-sourcing systems. It generates a query plan for developing a better balance between the latency and cost. The experimental outcome of the proposed methodology was validated on UCI automobile and Amazon Mechanical Turk (AMT) datasets. The proposed methodology saves 30%-40% of cost in crowd-sourcing query optimization compared to the existing methods.

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