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An Adaptive Memetic Algorithm for the Joint Allocation of Heterogeneous Stochastic Resources
IEEE Transactions on Cybernetics
|June 29, 2021
Summary
This study introduces an adaptive memetic algorithm (MA) to solve the joint allocation of stochastic resources (JASR) problem. The novel MA effectively handles complex resource interdependencies and constraints, yielding superior decision schemes.
Area of Science:
- Operations Research
- Computer Science
- Systems Engineering
Background:
- The joint allocation of stochastic resources (JASR) is a critical challenge in complex systems.
- Existing methods struggle with interdependencies and constraints inherent in heterogeneous resource allocation.
Purpose of the Study:
- To develop a robust mathematical model for JASR.
- To propose an effective adaptive memetic algorithm (MA) for solving the JASR problem.
Main Methods:
- A general mathematical model incorporating resource interdependencies, quantity, capability, and strategy constraints.
- An adaptive memetic algorithm (MA) utilizing multipermutation encoding.
- Permutation-based operators, hybrid initialization, adaptive replacement, and a restart strategy for population diversity.
Main Results:
- Computational experiments on 25 random test instances validated the MA's effectiveness.
- The proposed adaptive MA significantly outperformed prevailing solution methods for most instances.
- Statistical analysis (Wilcoxon rank-sum test) confirmed the superiority of the proposed MA.
Conclusions:
- The developed adaptive MA provides a powerful and effective solution for the complex JASR problem.
- The algorithm demonstrates strong performance in handling resource interdependencies and constraints.
- This research offers improved decision-making capabilities for stochastic resource allocation in complex systems.
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