Most existing particle swarm optimization (PSO) variants use a single learning strategy and a fixed neighborhood structure for all particles during the search process. The adoption of rigid learning pattern and communication topology may restrict the intelligence level of each particle, hence degrading the performance of PSO in solving the optimization problems with complicated fitness landscapes. Recent studies suggested that the employment of self-adaptive mechanism in adjusting the search strategy and topology connectivity of each particle along the search process may serve as a potential remedy to improve the performance of PSO, especially when dealing with complex problems. For this reason, a self-adaptive topologically connected (SATC)-based PSO equipped with an SATC module and an improved learning framework is proposed. The SATC module is envisioned to facilitate each particle to perform searching with different exploration and exploitation strengths by adaptively modifying its topology connectivity in different searching stages. A modified velocity update scheme and an alternative search operator are also introduced to formulate an improved learning framework to enhance the performance of proposed work further. Substantial numbers of benchmark functions and two real-world optimization problems are used to compare SATC-based PSO (SATCPSO) with several well-established PSO variants. Extensive studies have verified that SATCPSO is more competitive than its peers in most of the tested problems.

The cold-chain supply of perishable fruits continues to face challenges such as fuel wastage, fragmented stakeholder coordination, and limited real-time adaptability. Traditional solutions, based on static routing and centralized control, fall short in addressing the dynamic, distributed, and secure demands of modern food supply chains. This study presents a novel end-to-end architecture that integrates multi-agent reinforcement learning (MARL), blockchain technology, and generative artificial intelligence. The system features large language model (LLM)-mediated negotiation for inter-enterprise coordination, Pareto-based reward optimization balancing spoilage, energy consumption, delivery time, and climate and emission impact. Smart contracts and Non-Fungible Token (NFT)-based traceability are deployed over a private Ethereum blockchain to ensure compliance, trust, and decentralized governance. Modular agents—trained using centralized training with decentralized execution (CTDE)—handle routing, temperature regulation, spoilage prediction, inventory, and delivery scheduling. Generative AI simulates demand variability and disruption scenarios to strengthen resilient infrastructure. Experiments demonstrate up to 50% reduction in spoilage, 35% energy savings, and 25% lower emissions. The system also cuts travel time by 30% and improves delivery reliability and fruit quality. This work offers a scalable, intelligent, and sustainable supply chain framework, especially suitable for resource-constrained or intermittently connected environments, laying the foundation for future-ready food logistics systems.

The design of an effective antenna array is a major challenge encountered in most communication systems. A much-needed requirement is obtaining a directional and high-gain radiation pattern. This study deals with the design of a linear antenna array that radiates with reduced peak-side lobe levels (PSLL), decreases side-lobe average power with and without the first null beamwidth (FNBW) constraint, places deep nulls in the desired direction, and minimizes the close-in-side lobe levels (CSLL). The nature-inspired metaheuristic algorithm multi-verse optimization (MVO) is explored with other state-of-the-art algorithms to optimize the parameters of the antenna array. MVO is a global search method that is less prone to being stuck in the local optimal solution, providing a better alternative for beam-pattern synthesis. Eleven design examples have been demonstrated, which optimizes the amplitude and position of antenna array elements. The simulation results illustrate that MVO outperforms other algorithms in all the design examples and greatly enhances the radiation characteristics, thus promoting industrial innovation in antenna array design. In addition, the MVO algorithm’s performance was validated using the Wilcoxon non-parametric test.