Nature-inspired optimization algorithms: Challenges and open problems

Abstract

Many problems in science and engineering can be formulated as optimization problems, subject to complex nonlinear constraints. The solutions of highly nonlinear problems usually require sophisticated optimization algorithms, and traditional algorithms may struggle to deal with such problems. A current trend is to use nature-inspired algorithms due to their flexibility and effectiveness. However, there are some key issues concerning nature-inspired computation and swarm intelligence. This paper provides an in-depth review of some recent nature-inspired algorithms with the emphasis on their search mechanisms and mathematical foundations. Some challenging issues are identified and five open problems are highlighted, concerning the analysis of algorithmic convergence and stability, parameter tuning, mathematical framework, role of benchmarking and scalability. These problems are discussed with the directions for future research.

On the Cooperation Between Evolutionary Algorithms and Constraint Handling Techniques: A Further Empirical Study

“…DE, as a simple and efficient EA, was proposed by Storn and Price [17]. It can be seen as equation-based algorithms [15]. It mainly uses mutation and crossover operations to generate a trial vector to compete with the target vector and the better one will be preserved for next generation.…”

“…C 2 oDE sets a good example for EA and CHT cooperation, i.e., solution generating and solution choosing. As Yang mentioned [15], though researchers know the basic mechanisms of how the algorithms can work in practice, it is not quite clear why they work and under exact what conditions. So is there any inner mechanism behind this method, or which characteristics make the method work so well, is what we will study in this paper.…”

Immune Plasma Algorithm: A Novel Meta-Heuristic for Optimization Problems

“…Even though there are special analytic methods for solving some of the mentioned optimization problems, they generally utilize from the gradient-derivative based calculations for determining the search direction and qualities of the final solutions change dramatically with chosen start point or points [1], [2]. In order to outcome the possible drawbacks stemmed from the existing workflow of the classical methods, researchers focused on new problem solving techniques and meta-heuristic algorithms were proposed as an alternative to them [3]- [5].…”

“…When the short story of the meta-heuristics given above is investigated, it might be thought that existing algorithms are enough and there is no need for a new meta-heuristic technique. However, No-Free-Lunch (NFL) theorem states that each meta-heuristic algorithm has different capabilities and a single algorithm for solving all optimization problems with the highest efficiency does not exist [5]. As an expected result of this situation, designing new meta-heuristic algorithm after analyzing work-flow of an intelligent organizations of nature still protects its importance for further advances in computer, information and other engineering disciplines.…”

Breast Cancer Segmentation From Thermal Images Based on Chaotic Salp Swarm Algorithm

“…Those algorithms include Particle Swarm Optimization (PSO) [19], Artificial Bee Colony (ABC) [20], Ant Colony Algorithm (ACO) [21], Harris Hawks Optimization (HHO) [22], Whale optimization algorithm (WOA) [23], Grey Wolf Optimization (GWO) [18], [24], Moth-flame optimization (MFO) [25], Slime Mould Algorithm (SMA) [26], Bacterial Foraging Optimization (BFO) [27], and Slap Swarm Algorithm (SSA) [28]- [31]. Several comparative studies have investigated various metaheuristic techniques to compare their accuracy and effectiveness [32], [33]. One of the most promising metaheuristic algorithms is the SSA which is a nature-inspired optimizer that was proposed by Mirjalili et al [28].…”