Nature-Inspired Heuristic Frameworks Trends in Solving Multi-objective Engineering Optimization Problems

Abstract

Nowadays, nature-inspired artificial intelligent metaheuristic optimization algorithms (MHOAs) have gained many attentions from researchers all over the world due to their capabilities in solving various decision-making problems. These algorithms are inspired and modelled based on the searching behaviour of animals in real life. This review paper provides in-depth discussions on various challenges and breakthroughs in numerous state-of-the-art nature-inspired artificial intelligence (AI) algorithms in solving multi-objective optimization engineering problems with emphasis on the mathematical modelling and algorithm developments. From conventional analysis such as speeds and accuracies to relatively advanced benchmarks such as complexities and convergence patterns, the comparison criteria of population-based and nature-inspired search mechanisms have evolved in the effort to further enhance the overall performance and reachability of these heuristic algorithms. This paper provides a platform for young readers and new researches who are about to indulge in the realm of various AI optimization techniques. Comprehensive analysis and discussions are presented on various state-of-the-art methods, with possible fields of applications proposed. Suitability of search mechanisms to specific optimization problem categories has also been investigated and presented, with combined or hybrid methods under scrutiny.