A Modified Differential Evolution Algorithm Based on Improving A New Mutation Strategy and Self-Adaptation Crossover

Fadhil, Sadeer; Zaher, Hegazy; Ragaa, Naglaa; Oun, Eman

doi:10.1016/j.mex.2023.102276

Cited by 5 publications

(2 citation statements)

References 17 publications

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“…Similarly, the crossover rate determines the extent to which genetic information is exchanged between individuals. Self-adaptation of the crossover 83 rate can be achieved by monitoring the effectiveness of crossover operations in generating offspring solutions and adjusting the rate accordingly. So, by incorporating self-adaptive mechanisms [84][85][86] into the GA framework, it becomes possible to create more adaptive and robust optimization algorithms that can effectively tackle a wide range of transport optimization problems without the need for manual parameter tuning.…”

Section: Utilization Efficiencymentioning

confidence: 99%

Two-Stage Genetic Algorhitm for Optimization Logistics Network for Groupage Delivery

Malashin,

Tynchenko,

Masich

et al. 2024

Preprint

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This study explores groupage delivery optimization through a two-stage GA framework. The goal of optimization is to define locations of regional branch warehouses of Logistics Network to reduce transportation costs. The first stage of the GA focuses on identifying branch warehouses from a list of cities and uses the standard binary string approach, while the second stage solves Vehicle Routing Problem (VRP) using combinatorial GA. This research contributes to advancing logistics optimization methodologies and provides valuable insights for solving Location Routing Problem (LRP) which entails two problems: the Facility Location Problem (FLP) and the Multiple Vehicle Routing Problem (MVRP).

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Section: Utilization Efficiencymentioning

confidence: 99%

Two-Stage Genetic Algorhitm for Optimization Logistics Network for Groupage Delivery

Malashin,

Tynchenko,

Masich

et al. 2024

Preprint

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“…(vii). DE/current-to-best/2 (22) -it used a new mutation variants and a self-adapted crossover procedure. (viii).…”

Section: Introductionmentioning

confidence: 99%

Design Optimization Using Modified Differential Evolution Algorithm

Tiwari,

Mishra,

Parouha

2023

IJST

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Objectives:The key objective of this article is to suggest a modified differential evolution (MDE) algorithm for design problem optimization particularly reactor network design (RND) problem. Methods: During the few decades differential evolution (DE) algorithm achieved noticeable progress and solved a wide variety of optimization issues. However, the DE suffers from low diversification, poor exploration ability and stagnation. Hence, using concept of the particle swarm optimization mechanism (PSO), suggested MDE employed new mutation operator, to balance exploitation and exploration activities. Also, on the basis of time-varying scheme new mutation operator integrates new control parameter, to avoid stagnation. A group of 6 unconstrained benchmark functions are solved, to investigate the presentation of MDE algorithm. Moreover, its practical superiority is further verified by solving RND problem. Findings: The experiential results show that the suggested MDE performs well in each case of unconstrained benchmark functions with the highest rate of success. Moreover, optimize the RND problem very effectively with the lowest time (2.98s) and fewer number of function evaluations (12729). Furthermore, outcomes suggest that the proposed MDE exhibits a better or at least competitive performance compared to evolutionary algorithms. Novelty: The exploitation and exploration ability of the suggested MDE are balanced efficiently due to use of memory facts (i.e. novel mutation operator) and adapted (i.e. new time-varying) control parameters.

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Developments and Design of Differential Evolution Algorithm for Non-linear/Non-convex Engineering Optimization

Tiwari,

Mishra,

Parouha

2024

Arch Computat Methods Eng

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A Modified Differential Evolution Algorithm Based on Improving A New Mutation Strategy and Self-Adaptation Crossover

Cited by 5 publications

References 17 publications

Two-Stage Genetic Algorhitm for Optimization Logistics Network for Groupage Delivery

Two-Stage Genetic Algorhitm for Optimization Logistics Network for Groupage Delivery

Design Optimization Using Modified Differential Evolution Algorithm

Developments and Design of Differential Evolution Algorithm for Non-linear/Non-convex Engineering Optimization

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