Hot of the Press: A First Runtime Analysis of NSGA-III on a Many-Objective Multimodal Problem: Provable Exponential Speedup via Stochastic Population Update

Provable Speedups From Dynamic Population Sizes in Evolutionary Algorithms for Multiobjective Optimization

This paper introduces the bi-objective problem class CLIMB and analyzes the runtime of GSEMO and the widely used NSGA-II on this problem, and proves that GSEMO and NSGA-II-DYN, a version of NSGA-II with dynamic population sizes, can find the Pareto front of CLIMB in expected fitness evaluations.

A general hybrid framework for many-objective optimization: integrating local search into reference-vector-based evolutionary algorithms

Results show that integrating local search significantly enhances performance, while a principled method for setting hybrid parameters ensures robustness and reproducibility, highlighting the potential of combining mathematical programming techniques with evolutionary algorithms for high-dimensional many-objective optimization problems.

From Pareto to Neural: A Mathematical Survey of Multi-Objective Optimization Algorithms—With Applications to Software Testing

A systematic mapping study of multi-objective optimization algorithms, tracing their evolution from classical Pareto-based methods toward AI-driven and hybrid approaches, with software testing as the primary application domain, and outlining a research roadmap for the next generation of multi-objective optimization systems that combine the complementary mathematical strengths of neural function approximation and evolutionary diversity preservation.

A Comparative Study of Metaheuristics on Raspberry Pi 5: Results to Guide Optimization on Constrained Edge Devices

 This study investigates the efficiency of five metaheuristic algorithms, namely Differential Evolution (DE), Genetic Algorithm (GA), Grey Wolf Optimizer (GWO), Harmony Search (HS), and Particle Swarm Optimization (PSO), when deployed on a Raspberry Pi 5 edge device. The evaluation focuses on both optimization quality and computational cost, using four standard benchmark functions that represent a range of landscape characteristics: Sphere, Rosenbrock, Rastrigin, and Ackley. Each function is tested at dimensions 10, 30, and 50 to probe scalability. In addition to objective values, the experiments collect per-iteration processor usage and memory (RAM) to provide a practical view of runtime overhead under constrained resources. Among the five candidates, GWO consistently delivers the fastest or near-fastest convergence while keeping variability tight. Its trajectories show smooth descent across functions and dimensions, paired with comparatively modest CPU and RAM footprints. PSO typically ranks second in speed with stable dynamics, though brief CPU spikes often appear at early iterations as swarms synchronize. DE demonstrates resilience on rugged functions but generally requires more iterations to close the final gap. GA and HS can reach competitive objective values on some settings, yet they display wider dispersion and higher overhead at larger dimensions, which reduces their suitability for small devices. Overall, the evidence indicates that GWO is the most efficient choice for edge deployment on Raspberry Pi 5, striking a favorable balance between convergence speed, stability, and resource usage. PSO is a strong alternative when slightly higher processor activity is acceptable. These findings support the adoption of lightweight, variance-stable metaheuristics for edge optimization workloads where CPU and memory budgets are tight.

Benchmarking a standard genetic algorithm for real-world university class timetabling

The university class timetabling is known to be a classic example of a problem in combinatorial optimization, where an allocation of classes to time periods, classrooms, and tutors is needed. Although modern works mostly concentrate on designing various hybrid or specialized algorithms and metaheuristics, one should also examine the performance of the original algorithm to obtain a baseline metric for future comparisons. This paper considers a simple application of the unaltered SGA on Dataset A that has 38 subjects, 8 tutors, 8 classrooms, five days of operation, and two types of classes (theoretical and practical). As far as scalability was concerned, we have tested several population sizes for the algorithm, namely 15, 30, 60, and 120. Performance metrics such as fitness and the rate of convergence were measured according to the value of the fitness function and runtime as well as memory usage. It could be demonstrated that despite being extremely simple, classical SGA produces valid solutions for this problem but is quite sensitive to changes in population size. To make the analysis more robust, the paper also includes a very basic comparison experiment involving Simulated Annealing as another basis of evaluation.

Extending Weighted Heuristic Search to Bi-Objective Search Problems