TY - JOUR
T1 - Improving multi-criterion optimization with chaos
T2 - a novel Multi-Objective Chaotic Crow Search Algorithm
AU - Hinojosa, Salvador
AU - Oliva, Diego
AU - Cuevas, Erik
AU - Pajares, Gonzalo
AU - Avalos, Omar
AU - Gálvez, Jorge
PY - 2018/4/1
Y1 - 2018/4/1
N2 - This paper presents two multi-criteria optimization techniques: the Multi-Objective Crow Search Algorithm (MOCSA) and an improved chaotic version called Multi-Objective Chaotic Crow Search Algorithm (MOCCSA). Both methods MOCSA and MOCCSA are based on an enhanced version of the recently published Crow Search Algorithm. Crows are intelligent animals with interesting strategies for protecting their food hatches. This compelling behavior is extended into a Multi-Objective approach. MOCCSA uses chaotic-based criteria on the optimization process to improve the diversity of solutions. To determinate if the performance of the algorithm is significantly enhanced, the incorporation of a chaotic operator is further validated by a statistical comparison between the proposed MOCCSA and its chaotic-free counterpart (MOCSA) indicating that the results of the two algorithms are significantly different from each other. The performance of MOCCSA is evaluated by a set of standard benchmark functions, and the results are contrasted with two well-known algorithms: Multi-Objective Dragonfly Algorithm and Multi-Objective Particle Swarm Optimization. Both quantitative and qualitative results show competitive results for the proposed approach.
AB - This paper presents two multi-criteria optimization techniques: the Multi-Objective Crow Search Algorithm (MOCSA) and an improved chaotic version called Multi-Objective Chaotic Crow Search Algorithm (MOCCSA). Both methods MOCSA and MOCCSA are based on an enhanced version of the recently published Crow Search Algorithm. Crows are intelligent animals with interesting strategies for protecting their food hatches. This compelling behavior is extended into a Multi-Objective approach. MOCCSA uses chaotic-based criteria on the optimization process to improve the diversity of solutions. To determinate if the performance of the algorithm is significantly enhanced, the incorporation of a chaotic operator is further validated by a statistical comparison between the proposed MOCCSA and its chaotic-free counterpart (MOCSA) indicating that the results of the two algorithms are significantly different from each other. The performance of MOCCSA is evaluated by a set of standard benchmark functions, and the results are contrasted with two well-known algorithms: Multi-Objective Dragonfly Algorithm and Multi-Objective Particle Swarm Optimization. Both quantitative and qualitative results show competitive results for the proposed approach.
KW - Chaotic maps
KW - Crow Search Algorithm
KW - Evolutionary algorithms
KW - Multi-Objective optimization
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U2 - 10.1007/s00521-017-3251-x
DO - 10.1007/s00521-017-3251-x
M3 - Article
AN - SCOPUS:85032387332
VL - 29
SP - 319
EP - 335
JO - Neural Computing and Applications
JF - Neural Computing and Applications
SN - 0941-0643
IS - 8
ER -