Abstract
Glowworm swarm optimization (GSO) and bacterial foraging optimization algorithm (BFOA) are two popular swarm intelligence optimization algorithms (SIOAs). However, both GSO and BFOA show some difficulties when solving many-objective optimization problems (MaOPs). To challenge MaOPs, a coupling approach based on GSO and BFOA is proposed in this paper. To implement the coupling method, an external archive is established to save the best solutions found so far. The internal populations in GSO and BFOA can exchange the search information with the external archive in the evolutionary process. Simulation experiments are verified on two benchmark sets (DTLZ and WFG) with 3 to 15 objectives. The performance of our approach is compared with five other famous algorithms including NSGA-III, KnEA, MOEA/D-DE, GrEA and HypE. Results prove the effectiveness of our approach.
Highlights
We focus on improving swarm intelligence optimization algorithms (SIOAs) for solving many-objective optimization problems (MaOPs)
The above results demonstrate that the coupling approach can effectively improve the performance of glowworm swarm optimization (GSO) and bacteria foraging optimization algorithm (BFOA) on MaOPs
WORK The original GSO or BFOA shows some difficulties in solving MaOPs
Summary
As are mostly encouraged by the behaviors of biological swarm systems (e.g. bird flocking, foraging and courtship). When projects or systems in real-life become large, some very complex optimization problems have emerged, such as large-scale optimization problems [12], [13] and MaOPs [14], [15] For these problems, the performance of most SIOAs encounters great challenges [16]–[18]. For the above mentioned many-objective optimization algorithms (MaOEAs), few of them are SIOAs. In order to make SIOAs possible for solving MaOPs, some improved strategies were proposed [31]–[33]. In [34], Xiang et al introduced decomposition into ABC to solve MaOPs. indicatorbased set and reference-point are combined with PSO for many-objective optimization [35]–[37]. We focus on improving SIOAs for solving MaOPs. It is difficult to use one algorithm to solve all kinds of optimization problems.
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