Analysis and mitigation of greenhouse gases by replacing traditional energy with a hybrid energy system using battery optimization by Madhusudhanan Sampath, Amalorpava Mary Rajee Samuel, Yamuna Devi M.M, Senthil Murugan

“…The paper presents an improved Lyapunov optimisation (LO) algorithm to optimally estimate the number of photovoltaic panels and battery banks for enhanced energy management. …”
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Local Search-Based Metaheuristic Methods for the Solid Waste Collection Problem by Haneen Algethami

“…The solid waste collection problem refers to truck route optimisation to collect waste from containers across various locations. …”
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Demonstration Platform to Emulate Cognitive GEO-LEO Dual Satellite System by Kithmini Weththasinghe, Ying He, Quynh Tu Ngo, Beeshanga Jayawickrama, Eryk Dutkiewicz

“…Furthermore, the performance of the proposed sensing algorithm is assessed based on missed detection rates. …”
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Metaheuristic-Based Cepstral Sensing Technique for Prolonging Network Lifetime and Mitigating Primary User Emulation Attacks in Cognitive Radio Sensor Network by V. Abilasha, A. Karthikeyan

“…Whale optimisation algorithm maintains network performance and energy efficiency while refining sensing parameters and enhancing decision-making. …”
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Grey-Box Modelling of District Heating Networks Using Modified LPV Models by Olamilekan E. Tijani, Sylvain Serra, Patrick Lanusse, Rachid Malti, Hugo Viot, Jean-Michel Reneaume

“…Given the dynamic nature of DHNs and the challenges in predicting disturbances, a dynamic real-time optimisation (DRTO) approach is proposed. However, this research does not implement DRTO; instead, it develops a fast grey-box linear parameter varying (LPV) model for future integration into the DRTO algorithm. …”
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Smart forest monitoring: A novel Internet of Things framework with shortest path routing for sustainable environmental management by Alireza Etaati, Mostafa Bastam, Ehsan Ataie

“…Additionally, the placement of gateway nodes is optimised using meta‐heuristic algorithms, including particle swarm optimisation (PSO), harmony search algorithm (HSA), and ant colony optimisation for continuous domains (ACOR), with ACOR emerging as the most effective. …”
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Optimization of Adversarial Reprogramming for Transfer Learning on Closed Box Models by Alexander Bott, Moritz Siems, Alexander Puchta, Jurgen Fleischer

“…While it has been shown that this concept can work well in image classification, and a further modification has been developed to classify time series features, this work focuses primarily on the remaining key challenges such as the selection and comparison of appropriate loss functions, the optimisation of hyperparameters using Bayesian methods, and data labelling in the absence of ground truth. …”
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Hybrid multi-objective node deployment for energy-coverage problem in mobile underwater wireless sensor networks by Salmah Fattah, Ismail Ahmedy, Mohd Yamani Idna Idris, Abdullah Gani

“…The results and comparative analysis indicate that the Multi-Objective Optimisation Genetic Algorithm based on Adaptive Multi-Parent Crossover and Fuzzy Dominance (MOGA-AMPazy) is a better solution to the multi-objective sensor node deployment problem, outperforming the non-dominated sorting genetic algorithm II, SPEA2 and MOEA/D algorithms. …”
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Parameter Tuning for Local-Search-Based Matheuristic Methods by Guillermo Cabrera-Guerrero, Carolina Lagos, Carolina Castañeda, Franklin Johnson, Fernando Paredes, Enrique Cabrera

“…Algorithms that aim to solve optimisation problems by combining heuristics and mathematical programming have attracted researchers’ attention. …”
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Antioptimisation of Trusses Using Two-Level Population-Based Incremental Learning by Phinit Tontragunrat, Sujin Bureerat

“…There have been several ways to deal with such optimisation problems, and one of the approaches is an antioptimisation process. …”
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Distribution network planning considering active response of EVs and DTR of cables and transformers by Chen Pan, Weijiang Chen, Chengke Zhou, Wenjun Zhou

“…In this paper, we propose a model considering the active response of EVs, and a bi‐level DNP model incorporating the DTR of cables and transformers, in the upper level, the Prim algorithm is embedded into the particle swarm optimisation (PSO) algorithm to obtain an initial grid topology; in the lower level, types of cables and transformers as well as the installation of DTR equipment are determined, second‐order cone (SOC) relaxation and linearisation of the variables product are then carried out to meet the non‐linear constraints of cables and transformers, and the upper and lower models are solved in an iterative manner. …”
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A high-entropy image encryption scheme using optimized chaotic maps with Josephus permutation strategy by Nawal El Ghouate, Mohamed Amine Tahiri, Ahmed Bencherqui, Hanaa Mansouri, Ahmed El Maloufy, Hicham Karmouni, Mhamed Sayyouri, Sameh Askar, Mohamed Abouhawwash

“…This algorithm uses Kepler Chaotic Optimisation Algorithm (CKOA) to select the most suitable chaotic maps, guaranteeing optimal diffusion and complex pixel confusion. …”
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Sustainable THz SWIPT via RIS-Enabled Sensing and Adaptive Power Focusing: Toward Green 6G IoT by Sunday Enahoro, Sunday Cookey Ekpo, Mfonobong Uko, Fanuel Elias, Rahul Unnikrishnan, Stephen Alabi, Nurudeen Kolawole Olasunkanmi

“…A weighted MMSE inner loop maximizes the data rate, while an outer alternating optimisation applies semidefinite relaxation to enforce passive-element constraints and SAR compliance. …”
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Flexibility in load demand and PHEV parameters for clean and economic microgrid operation by Bishwajit Dey, Srikant Misra, Arnab Pal

“…This paper offers a novel one-to-one-based optimisation algorithm for minimising the generating cost of an MG system while considering various PHEV operational factors and DSM tactics. …”
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Prioritising explainable AI-driven recommendations with knowledge graphs and reinforcement learning by Neeraj Tiwary, Shahrul Azman Mohd Noah, Fariza Fauzi

“…To address this gap, we propose the Explainable Artificial Intelligence-driven Recommender System (XAIRec), which incorporates explainability mechanisms into knowledge graphs (KGs) and leverages knowledge graph embeddings (KGEs) alongside reinforcement learning (RL) to enhance both transparency and interpretability. By applying optimisation techniques that consider rewards, probabilities, information values, and affinity scores while minimising traversal hops within KG paths, XAIRec learns user preferences from historical data, determines optimal reasoning paths, and enhances recommendation quality by prioritising products with higher relevance. …”
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Abilities and limitations in counteracting urban heat islands by Michał Musiał, Anna Pomykała, Lech Lichołai

“…The ability to process vast amounts of data allows for more effective use of energy and water resources in buildings while continuously improving the algorithm. The literature review involved a comparison of current methods used to counteract urban heat islands, as well as existing and potential applications of artificial intelligence algorithms described in the Scopus and Web of Science databases. …”
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Investigating the dynamics and uncertainties in portfolio optimization using the Fourier-Millen transform. by Muhammad Hilal Alkhudaydi, Aiedh Mrisi Alharthi

“…These are then compared with other algorithms, such as vector autoregression, in portfolio optimisation tests. …”
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Cloud-edge hybrid deep learning framework for scalable IoT resource optimization by Umesh Kumar Lilhore, Sarita Simaiya, Yogesh Kumar Sharma, Anjani Kumar Rai, S. M. Padmaja, Khan Vajid Nabilal, Vimal Kumar, Roobaea Alroobaea, Hamed Alsufyani

“…This study proposes a novel optimisation approach utilising deep learning to tackle these challenges. …”
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Location allocation approach based on influence of points of dispensing by Nusaybah Alghanmi, Reem Alotaibi, Sultanah Alshammari, Arif Mahmood

“…A considerable amount of research has been conducted to determine the number and location of new PODs while keeping existing ones operational. A query-based approach needs data on the number of new PODs in advance, and an optimisation-based approach requires the specification of distance and percentage gap as constraints or objective to ensure the required population coverage is achieved. …”
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Research on SeaTreasure Target Detection Technology Based on Improved YOLOv7-Tiny by Xiang Shi, Yunli Zhao, Jinrong Guo, Yan Liu, Yongqi Zhang

“…Experimental results demonstrate that our optimized algorithm achieves a mAP0.5 of 79.5%, representing a 2.0 percentage point improvement over the baseline YOLOv7-tiny model (77.5%) while maintaining computational efficiency at 45.2 GFLOPs. …”
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