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Multi-objective Ant Colony Optimization: Review
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AI
DOI：10.1007/s11831-024-10178-4.png)
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Abstract
En 中文
Ant colony optimization (ACO) algorithm is one of the most popular swarm-based algorithms inspired by the behavior of an ant colony to find the shortest path for food. The multi-objective ACO (MOACO) is a modified variant of ACO introduced to deal with multi-objective optimization problems (MOPs). The MOACO is seeking to find a set of solutions that achieve trade-offs between the different objectives, which help the decision-makers select the most appreciated solution according to their preferences. Recently, a large number of MOACO research works have been published in the literature, reaching 384 research papers according to the SCOPUS database. In this review paper, 189 different research works of MOACOs published in only scientific journals are considered. Through this research, researchers will gain insights into the expansion of MOACO, the theoretical foundations of MOPs and the MOACO algorithm, various MOACO variants documented in existing literature will be reviewed, and the specific application domains where MOACO has been implemented will be summarized. The critical discussion of the MOACO advantages and limitations is analyzed to provide better insight into the main research gaps in this domain. Finally, the conclusion and some possible future research directions of MOACO are also given in this work.
Keywords:
SHOP SCHEDULING PROBLEM
VEHICLE-ROUTING PROBLEM
OPTIMAL-DESIGN
RESOURCE-ALLOCATION
DISTRIBUTION-SYSTEMS
GENETIC ALGORITHMS
SENSOR NETWORKS
MODEL
TIME
CONSOLIDATION
Journal
IF:
12.1
Papers:
1.8K
Citations:
1.2W
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