This paper presents a comprehensive review of multimodal emotion recognition (MER), a process that integrates multiple data modalities such as speech, visual, and text to identify human emotions. Grounded in biomimetics, the survey frames MER as a bio-inspired sensing paradigm that emulates the way humans seamlessly fuse multisensory cues to communicate affect, thereby transferring principles from living systems to engineered solutions. By leveraging various modalities, MER systems offer a richer and more robust analysis of emotional states compared to unimodal approaches. The review covers the general structure of MER systems, feature extraction techniques, and multimodal information fusion strategies, highlighting key advancements and milestones. Additionally, it addresses the research challenges and open issues in MER, including lightweight models, cross-corpus generalizability, and the incorporation of additional modalities. The paper concludes by discussing future directions aimed at improving the accuracy, explainability, and practicality of MER systems for real-world applications.

Biomimetic optimization transfers biological information-processing mechanisms into computational systems. Ant colony optimization (ACO) is a canonical example: artificial ants functionally abstract pheromone-mediated stigmergy, decentralized exploration, trail decay through algorithmic evaporation, and adaptive path reinforcement. Building on this functional biological analogue, we present a controlled cross-paradigm evaluation of routing-heuristic generation. A standardized interface embeds human-designed rules, the genetic programming hyper-heuristic GHPP, a resource-constrained DeepACO-MLP proxy, and an offline ReEvo-style proxy into the same ACO solver. The methods are evaluated on held-out TSP and CVRP instances in terms of solution quality, reported generation or training cost, interpretability, and cross-scale behavior under a matched distribution. GHPP yields the shortest routes at all tested scales; the ReEvo-offline proxy and strong human-designed rules generally form a second tier, whereas the resource-constrained neural proxy degrades markedly as problem size increases. These results do not establish an intrinsic ranking of full-capability paradigms. Instead, they show that method selection depends on the operating constraint and on evidence provenance: longer locally measured offline search favors GHPP, while auditable explicit rules characterize the human and ReEvo-offline proxies. By holding the ant-inspired execution mechanism fixed and varying the source of heuristic information, the benchmark clarifies how evolutionary, neural, and LLM-style design strategies interact with a common biomimetic substrate.

This study examines whether candidate-level gain–loss information improves search for cost-aware portfolio rebalancing. We propose Risk-Sensitive Foraging Artificial Bee Colony (RSF-ABC) for long-only portfolios with exact cardinality and active-weight bounds. A bounded state derived from the Omega objective controls the probability of heavy-tailed exploration versus conservative best-guided search. A state-independent mixture of the same operators isolates the value of state conditioning. Monthly evaluation on U.S. industry portfolios uses 504-day formation windows and proportional transaction costs. Mean net Omega is 1.1635 for RSF-ABC, 1.1284 for Standard ABC, 1.1342 for Fixed-Mix-ABC, 1.1410 for particle swarm optimization, and 1.1450 for differential evolution. Monthly turnover is 0.4780 versus 0.5570 for Standard ABC. The proposed method leads in five of seven boundary conditions; deterministic alternatives lead in two. At 49 assets, its reported median runtime is 1.11 s versus 1.08 s for Standard ABC. These results support the evaluated state–operator coupling.