An adaptive local search method for multiobjective PSO
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An adaptive local search method for multiobjective PSO using the time variance search space index to improve the diversity of solutions and convergence
A novel MOPSO with enhanced local search ability and parameter-less sharing
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The proposed approach estimates the density of the particles’ neighborhood in the search space. Initially, the proposed method accurately determines the crowding factor of the solutions; in later stages, it effectively guides the entire swarm to converge close to the true Pareto front
A coevolutionary technique based on multiswarm particle swarm optimization
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The authors combined their proposed algorithm with special boundary constraint processing and a velocity update strategy to help with the diversity and convergence speed
FC-MOPSO algorithm can work on a mix-up of constrained, unconstrained, continuous and/or discrete, single-objective, multiobjective optimization problems algorithm that can work on a mix-up of constrained, unconstrained, continuous, and/or discrete optimization problems
A novel particle swarm optimization algorithm with multiple learning strategies (PSO-MLS)
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The authors proposed an approach for multiswarm PSO that pairs the velocity update of some swarms with different methods such as the periodically stochastic learning strategy or random mutation learning strategy.
Cellular Learning Automata (CLA) for multiswarm PSO
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Each swarm is placed on a cell of the CLA, and each particle’s velocity is affected by some other particles. The connected particles are adjusted overtime via periods of learning
Improved particle swarm optimization algorithm based on dynamical topology and purposeful detecting.
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In order to balance the search capabilities between swarms. The extensive experimental results illustrate the effectiveness and efficiency of the three proposed strategies used in MSPSO
To modify the velocity update equation to increase search information and diversity solutions to avoid local Pareto front. The results show superior performance in solving optimization problems
The authors proposed a strategy to improve the speed of convergence of multiswarm PSO for robots’ movements in a complex environment with obstacles. Additionally, the authors combine the local search strategy with multiswarm PSO to prevent the robots from converging at the same locations when they try to get to their targets
Based on new leader selection strategy to improved particle swarm optimization
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The algorithm used triangular distance to select leader individuals that cover different regions in the Pareto frontier. The authors also included an update strategy for with respect to their connected leaders. MOPSO tridist was shown to outperform other multiobjective PSOs, and the authors illustrated the algorithm’s application with the digital watermarking problem for RBG images
For solving the problem of transmitting information on networks. The work result proves that the proposed discrete PSO outperforms Simulated Annealing (SA)
Application (multichoice question test extraction)
Novel approach of particle swarm optimization (PSO)
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The dynamic question generation system is built to select tailored questions for each learner from the item bank to satisfy multiple assessment requirements. The experimental results show that the PSO approach is suitable for the selection of near-optimal questions from large-scale item banks
The authors used particle swarm optimization to generate tests with approximating difficulties to the required levels from users. The experiment result shows that PSO gives the best performance concerning most of the criteria
The authors use particle swarm optimization to generate multitests with approximating difficulties to the required levels from users. In this parallel stage, migration happens between swarms to exchange information between running threads to improve the convergence and diversities of solutions
