Nature-inspired optimization algorithms: Challenges and open problems

TL;DR: Adaptive Neuro-Fuzzy Inference System (ANFIS) as mentioned in this paper combines advantages of both Artificial Neural Networks (ANNs) and Fuzzy Logic (FL) in a single framework.

TL;DR: In this paper, a tunicate swarm algorithm (TSA) with a local escaping operator (LEO) was proposed for overcoming the weaknesses of the original TSA. The LEO strategy in TSA-LEO prevents searching deflation in TSA and improves the convergence rate and local search efficiency of swarm agents.

TL;DR: In this article, the authors compared the performance of two nature-inspired algorithms for getting optimal coverage in WSNs, i.e., the combined improved genetic algorithm and binary ant colony algorithm, and the lion optimization (LO) algorithm.

TL;DR: The overall optimization results demonstrate that AISA possesses a strong and robust capability to produce superior performance over other competitor metaheuristic algorithms in solving various complex numerical optimization problems.

TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.

TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.

TL;DR: In this article, a new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented, which requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.

TL;DR: A snapshot of particle swarming from the authors’ perspective, including variations in the algorithm, current and ongoing research, applications and open problems, is included.