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In real world applications, there are many multi-objective optimization problems. Most existing multi-objective optimization algorithms focus on improving the diversity, spread and convergence of the solutions in the objective space. Few works study the distribution of solutions in the decision space. In practical applications, some multi-objective problems have different Pareto sets with the same...
In this paper, a fast-sorting method called summation of normalized objectives and diversified selection (SNOV-DS) is embedded in Comprehensive Learning Particle Swarm Optimization (CLPSO) to solve multi-objective problems. Due to this method, the simulation time will be decreased. The convergence to true Pareto front and the spread of solutions can also be improved. The algorithm is tested on a set...
In real world optimization, many problems are not only target on finding one global peak, but also multiple global/local peaks. These problems are referred as multi-modal optimization problems. Various techniques that commonly known as niching are proposed to solve multi-modal problems. Species-based differential evolution (SDE) is one of the recent algorithms that use the notion of speciation for...
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