Large-scale multiobjective optimization with adaptive competitive swarm optimizer and inverse modeling

TL;DR: This paper reviews a decade of Competitive Swarm Optimizer (CSO) research, synthesizing key variants, principles, and applications, including single-objective, multi-objective, and engineering optimization problems, and provides a taxonomy and comparative analyses for future investigation.

TL;DR: This study proposes LSMCSO-SS, a modified competitive swarm optimizer guided by space sampling for large-scale multi-objective optimization, outperforming seven state-of-the-art algorithms on nine benchmark problems with up to 5000 decision variables.

TL;DR: This study proposes an adaptive multi-objective competitive swarm optimization algorithm for municipal solid waste incineration, achieving 4.36% heat improvement and 4.13% exhaust gas reduction through kinematic analysis and multi-strategy learning.

TL;DR: In this paper, the authors give a basic introduction to Gaussian Process regression models and present the simple equations for incorporating training data and examine how to learn the hyperparameters using the marginal likelihood.

TL;DR: A real-coded crossover operator is developed whose search power is similar to that of the single-point crossover used in binary-coded GAs, and SBX is found to be particularly useful in problems having mult ip le optimal solutions with a narrow global basin where the lower and upper bo unds of the global optimum are not known a priori.

TL;DR: This paper surveys the development ofMOEAs primarily during the last eight years and covers algorithmic frameworks such as decomposition-based MOEAs (MOEA/Ds), memetic MOEas, coevolutionary MOE As, selection and offspring reproduction operators, MOE as with specific search methods, MOeAs for multimodal problems, constraint handling and MOE

TL;DR: In the proposed algorithm, a scalarization approach, termed angle-penalized distance, is adopted to balance convergence and diversity of the solutions in the high-dimensional objective space, and reference vectors are effective and cost-efficient for preference articulation, which is particularly desirable for many-objective optimization.