A self-organizing multimodal multi-objective pigeon-inspired optimization algorithm

Abstract

Multi-objective optimization algorithms have recently attracted much attention as they can solve problems involving two or more conflicting objectives effectively and efficiently. However, most existing studies focus on improving the performance of the solutions in the objective spaces. This paper proposes a novel multimodal multi-objective pigeon-inspired optimization (MMOPIO) algorithm where some mechanisms are designed for the distribution of the solutions in the decision spaces. First, MMOPIO employs an improved pigeon-inspired optimization (PIO) based on consolidation parameters for simplifying the structure of the standard PIO. Second, the self-organizing map (SOM) is combined with the improved PIO for better control of the decision spaces, and thus, contributes to building a good neighborhood relation for the improved PIO. Finally, the elite learning strategy and the special crowding distance calculation mechanisms are used to prevent premature convergence and obtain solutions with uniform distribution, respectively. We evaluate the performance of the proposed MMOPIO in comparison to five state-of-the-art multi-objective optimization algorithms on some test instances, and demonstrate the superiority of MMOPIO in solving multimodal multi-objective optimization problems.

An overview of protected satellite communications in intelligent age

“…Multimodal machine learning aims at the interpretation and reasoning of multimodal messages and helps people to better understand the world [86]. In the protected SatCom systems, it mainly plays the role of collecting and processing the data from the satellite cross-links.…”

Multi-Objective Particle Swarm Optimization Based on Gaussian Sampling

“…To demonstrate the effectiveness of algorithm, the proposed GS-MOPSO is compared with the following twelve multimodal multi-objective algorithms: ZS-MO_Ring_PSO_SCD (denoted as ZS-MRPS for simplicity) [51], DE_RLRF [40], SSMOPSO [17], MMOPIO [52], MMODE [53], TriMOEATA&R [15], PEN-MOBA [54], NMOHSA [55], SMPSO-MM [16], MO_Ring_PSO_SCD (denoted as MRPS) [11], DN-NSGAII [10], and Omnioptimizer [33].…”

An Evolutionary Algorithm with Clustering‐Based Assisted Selection Strategy for Multimodal Multiobjective Optimization

“…Moreover, in MOEA/D-AD [31], by incorporating the addition and deletion operators into MOEA/D, it can obtain multiple nondominated solutions being different in decision space, which strengthens the diversity of population in decision space. Furthermore, there are other heuristic algorithms having been getting more attention for MMOPs, that is, particle swarm optimization (PSO) algorithms [32][33][34]. For example, a PSO algorithm is proposed in [32] as an effective niching algorithm, which does not need any niching parameter.…”

“…In MO_Ring_PSO_SCD [33], a ring topology and special crowding distance are incorporated into PSO algorithm, helping to ensure greater diversity. Similarly, in [34], the neighborhood in decision space is constructed by a self-organizing map network, aiming to approximate multiple PSs for solving MMOPs. Recently, in TriMOEA-TA&R [35], two archives are used to cooperatively balance the convergence in objective space and the diversity in both decision and objective spaces.…”

“…However, for tackling MMOPs with at least one local PS, once global PSs are found in evolutionary process of the above multimodal MOEAs (MMOEAs) [29][30][31][32][33][34][35], all local PSs will not survive in next-generation population as local PSs are often dominated by global PS and they are usually replaced by some solutions with good convergence. To solve MMOPs, we develop a novel evolutionary algorithm with clustering-based assisted selection strategy for multimodal multiobjective optimization (MMOEA-CAS).…”