Adaptive weighted dynamic differential evolution algorithm for emergency material allocation and scheduling

Wang, Tiejun ORCID logoORCID: https://orcid.org/0000-0002-1062-9051, Wu, Kaijun, Du, Tiaotiao and Cheng, Xiaochun ORCID logoORCID: https://orcid.org/0000-0003-0371-9646 (2022) Adaptive weighted dynamic differential evolution algorithm for emergency material allocation and scheduling. Computational Intelligence, 38 (3) . pp. 714-730. ISSN 0824-7935 [Article] (doi:10.1111/coin.12389)

Abstract

Emergency material allocation and scheduling is a combination optimization problem, which is essentially a Non-deterministic Polynomial (NP) problem. Aiming at the problems such as slow convergence, easy prematurely falling into local optimum, and parameter constraints to solve high-dimensional and multi-modal combination optimization problems, this article proposes an adaptive weighted dynamic differential evolution (AWDDE) algorithm. The algorithm uses a chaotic mapping strategy to initialize the population. By weighting the standard differential evolution (DE) mutation strategy, a new weighted mutation operator is proposed. The scaling factor and cross probability can be adaptively adjusted. A disturbance operator is introduced to randomly generate the perturbation mutation and to accelerate the premature individuals to jump out of the local optimum. The algorithm is applied to the problem of emergency material allocation and scheduling, and a two-stage emergency material allocation and scheduling model is established. Compared with the standard DE algorithm and the chaos adaptive particle swarm algorithm, the results show that the AWDDE algorithm has the characteristics of stronger global optimization ability and faster convergence speed compared with other optimization algorithms, which provide assistance for smart cities research, including smart city services, applications, case studies, and policymaking considerations for emergency management.

Item Type: Article
Keywords (uncontrolled): adaptive dynamic weighting, differential evolution algorithm, emergency material allocation and scheduling, smart cities research
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 35329
Useful Links:
Depositing User: Jisc Publications Router
Date Deposited: 30 Jun 2022 14:26
Last Modified: 30 Jun 2022 14:26
URI: https://eprints.mdx.ac.uk/id/eprint/35329

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