A Robust Archived Differential Evolution Algorithm for Global Optimization Problems

Abstract

<p class="MsoNormal" style="text-align: left; margin: 0cm 0cm 0pt; layout-grid-mode: char;" align="left"><span class="text"><span style="font-family: ";Arial";,";sans-serif";; font-size: 9pt;">A robust archived differential evolution algorithm is put forward by means of embedding a flexibility processing operator and an efficiency processing operator based on original DE and ADE. A special constraint-handling mechanism based on dynamic penalty functions and fitness calculation of individuals is adopted in the proposed method to deal with various constraints effectively, which is further extended by means of a flexibility processing operator so as to make it suitable for different type problems, including those with or without constraint(s) and those with continuous, discrete or mixed discrete-continuous variables. Furthermore, an archive of solutions is maintained during the evolutionary process so as to keep the useful information of previous solutions and local optima for the estimation of new solutions. Based on the archive of solutions, an iterative control operator and an efficiency processing operator are designed in the algorithm. The former guides the evolutionary process towards a promising search space, avoiding unnecessary and worthless search. The latter improves the local searching efficiency and the final searching quality. Experimental results based on a suite of six well-known optimization problems reveal that the proposed algorithm is robust, effective, efficient and suitable for different type global optimization problems.</span></span><span style="font-family: ";Arial";,";sans-serif";; font-size: 9pt;"></span></p&gt

Modified Flower Pollination Algorithm for Global Optimization

“…One of those schemes, having a high ability for the exploration, is "DE/rand/1", which explores the regions around one selected randomly from the population [62]. There are several DE variants based on various updating schemes or hybridization between DE, and some effective techniques have been extensively applied to tackle global optimization [63][64][65][66][67][68][69][70][71][72][73]. Since our proposed, MFPA suffers from a problem in the exploration operator, and the DE's updating scheme "DE/rand/1", have more direction to the exploration operator than the exploitation.…”