A Multi-Objective Anti-Predatory NIA for E-Commerce Logistics Optimization Problem

Rohit Kumar Sachan and Dharmender Singh Kushwaha
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Rohit Kumar Sachan: Motilal Nehru National Institute of Technology, India
Dharmender Singh Kushwaha: Motilal Nehru National Institute of Technology, India

International Journal of Applied Metaheuristic Computing (IJAMC), 2021, vol. 12, issue 4, 1-27

Abstract: Nature-inspired algorithms (NIAs) have established their promising performance to solve both single-objective optimization problems (SOOPs) and multi-objective optimization problems (MOOPs). Anti-predatory NIA (APNIA) is one of the recently introduced single-objective algorithm based on the self-defense behavior of frogs. This paper extends APNIA as multi-objective algorithm and presents the first proposal of APNIA to solve MOOPs. The proposed algorithm is a posteriori version of APNIA, which is named as multi-objective anti-predatory NIA (MO-APNIA). It uses the concept of Pareto dominance to determine the non-dominated solutions. The performance of the MO-APNIA is established through the experimental evaluation and statistically verified using the Friedman rank test and Holm-Sidak test. MO-APNIA is also employed to solve a multi-objective variant of hub location problem (HLP) from the perspective of the e-commerce logistics. Results indicate that the MO-APNIA is also capable to finds the non-dominated solutions of HLP. This finds immense use in logistics industry.

Date: 2021
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