The field of evolutionary computation and optimization is experiencing significant growth, with a focus on improving the efficiency and effectiveness of algorithms. Recent studies have highlighted the importance of understanding population dynamics and the role of stagnation in evolutionary algorithms, challenging traditional beliefs that convergence implies optimality. New optimization algorithms, such as the Adaptive Bacterial Colony Optimisation (ABCO) and Artificial Protozoa Optimizer (APO), have been proposed, demonstrating competitive performance and adaptability. Additionally, research has focused on enhancing existing algorithms, such as the strength Pareto evolutionary algorithm 2 (SPEA2) and the NSGA-III, with proven approximation guarantees and improved runtime analyses. Noteworthy papers include: The paper on Stagnation in Evolutionary Algorithms, which highlights that stagnation can actually facilitate convergence. The ABCO algorithm, which outperforms established optimisation algorithms on benchmark functions. The paper on Proven Approximation Guarantees in Multi-Objective Optimization, which proves that SPEA2 can compute optimal approximations of the Pareto front in polynomial time.
Advancements in Evolutionary Computation and Optimization
Sources
Stagnation in Evolutionary Algorithms: Convergence $\neq$ Optimality
ABCO: Adaptive Bacterial Colony Optimisation
Proven Approximation Guarantees in Multi-Objective Optimization: SPEA2 Beats NSGA-II
Tight Runtime Guarantees From Understanding the Population Dynamics of the GSEMO Multi-Objective Evolutionary Algorithm
A First Runtime Analysis of NSGA-III on a Many-Objective Multimodal Problem: Provable Exponential Speedup via Stochastic Population Update
Scalable Speed-ups for the SMS-EMOA from a Simple Aging Strategy
Accelerating Evolution: Integrating PSO Principles into Real-Coded Genetic Algorithm Crossover
Algorithm Selection in Short-Range Molecular Dynamics Simulations
Artificial Protozoa Optimizer (APO): A novel bio-inspired metaheuristic algorithm for engineering optimization
Fluid Volume Assignment for Flow-Based Biochips: State-of-the-Art and Research Challenges
A New Scope and Domain Measure Comparison Method for Global Convergence Analysis in Evolutionary Computation
Learning-Based Approaches for Job Shop Scheduling Problems: A Review