Holonic optimization system for sustainable agriculture planning in Algeria

Abstract

Agricultural planning in Algeria remains largely centralized, facing limitations in adaptability, responsiveness, and sustainability. With rising environmental challenges and the need for local-level customization, traditional systems struggle to efficiently manage complex agricultural data and decision-making processes. This thesis proposes a novel holonic optimization system for sustainable agricultural planning, designed to bridge the gap between local needs and national strategies. Inspired by holonic and multi-agent system principles, the solution introduces a hierarchical model composed of four interdependent levels: local, district, regional, and national. Each level functions as an autonomous agent (holon) capable of making partial decisions that contribute to a global agricultural plan. To operationalize this model, real data was collected from the Wilaya of Tébessa, structured into regional catalogs and parcel-level datasets. These were then used to simulate and evaluate different optimization techniques, namely the Genetic Algorithm (GA), Weighted Sum Method (WSM), and the BKPACS algorithm, which is the proposed approach in this work. Comparative results demonstrated that the holonic system using BKPACS outperforms traditional methods in terms of execution time, sustainability, and solution quality. The integration of AUML diagrams and a structured database further enhanced system clarity and applicability. Ultimately, this research offers a scalable, intelligent, and practical solution for transforming agricultural planning in Algeria, paving the way for digital, data-driven decision support in the agricultural sector.