MMKE: Multi-trial vector-based monkey king evolution algorithm and its applications for engineering optimization problems

Abstract

Monkey king evolution (MKE) is a population-based differential evolutionary algorithm in which the single evolution strategy and the control parameter affect the convergence and the balance between exploration and exploitation. Since evolution strategies have a considerable impact on the performance of algorithms, collaborating multiple strategies can significantly enhance the abilities of algorithms. This is our motivation to propose a multi-trial vector-based monkey king evolution algorithm named MMKE. It introduces novel best-history trial vector producer (BTVP) and random trial vector producer (RTVP) that can effectively collaborate with canonical MKE (MKE-TVP) using a multi-trial vector approach to tackle various real-world optimization problems with diverse challenges. It is expected that the proposed MMKE can improve the global search capability, strike a balance between exploration and exploitation, and prevent the original MKE algorithm from converging prematurely during the optimization process. The performance of the MMKE was assessed using CEC 2018 test functions, and the results were compared with eight metaheuristic algorithms. As a result of the experiments, it is demonstrated that the MMKE algorithm is capable of producing competitive and superior results in terms of accuracy and convergence rate in comparison to comparative algorithms. Additionally, the Friedman test was used to examine the gained experimental results statistically, proving that MMKE is significantly superior to comparative algorithms. Furthermore, four real-world engineering design problems and the optimal power flow (OPF) problem for the IEEE 30-bus system are optimized to demonstrate MMKE’s real applicability. The results showed that MMKE can effectively handle the difficulties associated with engineering problems and is able to solve single and multi-objective OPF problems with better solutions than comparative algorithms.

Hyperparameter optimisation in differential evolution using Summed Local Difference Strings, a rugged but easily calculated landscape for combinatorial search problems

“…In [38], an ensemble of control parameters and mutation methods for DE has been proposed while considering the dynamic mutation strategies and set of values for control parameters. In [39], monkey king differential evolution using a multi-trial vector has been studied. The relation between exploitation and exploration depends upon control parameter and evolution strategy.…”

Optimizing a Multi-Layer Perceptron Based on an Improved Gray Wolf Algorithm to Identify Plant Diseases

“…This idea is mainly used to optimize the search space continuously by simulating biological evolution and genetic mechanisms [35,36]. Standard evolutionary computation algorithms include genetic algorithms, differential evolution algorithms, etc.…”

“…Different attack methods can enhance the diversity of wolf packs, and two other attack methods can effectively improve the algorithm's exploration ability. When the update phase is less (1/2 × maxiteration), select Equation (36) as the attacking method, and when the update phase is more (1/2 × maxiteration), the attacking method can be computed by Equation (37).…”

A Systematic Review of the Whale Optimization Algorithm: Theoretical Foundation, Improvements, and Hybridizations

“…SI algorithms are more applicable than the other two categories since they have similar structures, simplicity, adaptability, robustness, high-speed convergence, and few parameters [ 37 – 39 ]. Therefore, many SI algorithms have been developed to solve real-world continuous [ 40 – 43 ] and binary [ 44 – 48 ] optimization problems in different applications, such as the medical [ 49 , 50 ] and engineering [ 51 – 54 ] fields. However, SI algorithms suffer from inferior search strategies [ 55 ], which leads to premature convergence [ 56 ], an unbalancing issue [ 57 ], local optima trapping [ 58 ], and low population diversity [ 59 – 62 ].…”