F.2.7. Optimization
alireza Omidi nasab; Sajad Bastami; Rojiar Pir Mohammadiani; Mohammad Bagher Dowlatshahi; Seyedeh Zahra Mousavi
Abstract
Deep Neural Networks (DNNs) are increasingly deployed in safety-critical domains such as autonomous driving, healthcare, finance, and natural language processing, yet they remain vulnerable to adversarial attacks—subtle manipulations that can cause confident misclassifications or misleading predictions. ...
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Deep Neural Networks (DNNs) are increasingly deployed in safety-critical domains such as autonomous driving, healthcare, finance, and natural language processing, yet they remain vulnerable to adversarial attacks—subtle manipulations that can cause confident misclassifications or misleading predictions. This fragility poses a major barrier to building secure and trustworthy AI systems. Conventional defenses, including adversarial training and heuristic detection, often struggle to balance robustness, adaptability, and computational cost. To overcome these limitations, we propose a hybrid adaptive defense framework that unifies Ant Colony Optimization (ACO) with Reinforcement Learning (RL). ACO efficiently explores the high-dimensional space of defense hyperparameters to find globally optimal configurations, while RL enables dynamic, context-aware adaptation of defense strategies in real time. The proposed ACO-RL framework was rigorously evaluated across six diverse benchmark datasets spanning multiple data modalities: MNIST and CIFAR-10 (vision), IMDB and AG News (text), and Cora and Reddit-Binary (graph). Experimental results show that ACO-RL consistently enhances robustness against a wide spectrum of adversarial attacks, outperforming several state-of-the-art baselines. These findings highlight a promising pathway toward developing resilient, cross-domain AI systems capable of defending against evolving adversarial threats.
H.3.15.3. Evolutionary computing and genetic algorithms
M. B. Dowlatshahi; V. Derhami
Abstract
A combinatorial auction is an auction where the bidders have the choice to bid on bundles of items. The WDP in combinatorial auctions is the problem of finding winning bids that maximize the auctioneer’s revenue under the constraint that each item can be allocated to at most one bidder. The WDP ...
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A combinatorial auction is an auction where the bidders have the choice to bid on bundles of items. The WDP in combinatorial auctions is the problem of finding winning bids that maximize the auctioneer’s revenue under the constraint that each item can be allocated to at most one bidder. The WDP is known as an NP-hard problem with practical applications like electronic commerce, production management, games theory, and resources allocation in multi-agent systems. This has motivated the quest for efficient approximate algorithms both in terms of solution quality and computational time. This paper proposes a hybrid Ant Colony Optimization with a novel Multi-Neighborhood Local Search (ACO-MNLS) algorithm for solving Winner Determination Problem (WDP) in combinatorial auctions. Our proposed MNLS algorithm uses the fact that using various neighborhoods in local search can generate different local optima for WDP and that the global optima of WDP is a local optima for a given its neighborhood. Therefore, proposed MNLS algorithm simultaneously explores a set of three different neighborhoods to get different local optima and to escape from local optima. The comparisons between ACO-MNLS, Genetic Algorithm (GA), Memetic Algorithm (MA), Stochastic Local Search (SLS), and Tabu Search (TS) on various benchmark problems confirm the efficiency of ACO-MNLS in the terms of solution quality and computational time.
