A diversity enhanced multiobjective particle swarm optimization

Evaluation and benchmarking of many-objective optimization (MaOO) methods are complicated. The rapid development of new optimization algorithms for solving problems with many objectives has increas...

Review of the Research Landscape of Multi-Criteria Evaluation and Benchmarking Processes for Many-Objective Optimization Methods: Coherent Taxonomy, Challenges and Recommended Solution

In this paper, we propose an extended multi-objective version of single objective optimization algorithm called sperm swarm optimization algorithm. The proposed multi-objective optimization algorithm based on sperm fertilization procedure (MOSFP) operates based on Pareto dominance and a crowding factor, that crowd and filter out the list of the best sperms (global best values). We divide the sperm swarm into three equal parts, after that, different types of turbulence (mutation) operators are applied on these parts, such as uniform mutation, non-uniform mutation, and without any mutation. Our algorithm is compared against three well-known algorithms in the field of optimization. These algorithms are NSGA-II, SPEA2, and OMOPSO. These algorithms are compared using a very popular benchmark function suites called Zitzler-Deb-Thiele (ZDT) and Walking-Fish-Group (WFG). We also adopt three quality metrics to compare the convergence, accuracy, and diversity of these algorithms, including, inverted generational distance (IGD), spread (SP), and epsilon (∈). The experimental results show that the performance of the proposed MOSFP is highly competitive, which outperformed OMOPSO in solving problems such as ZDT3, WFG5, and WFG8. In addition, the proposed MOSFP outperformed both of NSGA-II or SPEA2 algorithms in solving all the problems.

Multi-Objective Optimization Algorithm Based on Sperm Fertilization Procedure (MOSFP)

The successful application of multi-objective evolutionary algorithms (MOEAs) in many kinds of multiobjective problems have attracted considerable attention in recent years. In this paper, an adaptive multi-objective evolutionary algorithm is proposed by incorporating the concepts of the grid system (denoted as AGMOEA). Based on grid, the objective space is divided into subspaces. Based on the quality and dominance relationship between subspaces, the evolutionary opportunities are dynamically allocated to different subspaces with an adaptive selection strategy. To improve the evolutionary efficiency, the evolutionary scheme and an external archive mechanism considering representative individuals are proposed. The experimental results on 21 benchmark problems demonstrate that the proposed algorithm is competitive or superior to the rival algorithms.

An adaptive multiobjective evolutionary algorithm based on grid subspaces

In order to improve the convergence and distribution of a many-objective evolutionary algorithm, this paper proposes an improved NSGA-III algorithm based on weight vector adjustment (called NSGA-III-WA). First, an adaptive weight vector adjustment strategy is proposed to decompose the objective space into several subspaces. According to different subspace densities, the weight vector is sparse or densely adjusted to ensure the uniformity of the weight vector distribution on the Pareto front surface. Secondly, the evolutionary model that combines the new differential evolution strategy and genetic evolution strategy is proposed to generate new individuals and enhance the exploration ability of the weight vector in each subspace. The proposed algorithm is tested on the optimization problem of 3–15 objectives on the DTLZ standard test set and WFG test instances, and it is compared with the five algorithms with better effect. In this paper, the Whitney–Wilcoxon rank-sum test is used to test the significance of the algorithm. The experimental results show that NSGA-III-WA has a good effect in terms of convergence and distribution.

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A Many-Objective Optimization Algorithm Based on Weight Vector Adjustment

In evolutionary multi-objective optimization, achieving a balance between convergence speed and population diversity remains a challenging topic especially for many-objective optimization problems (MaOPs). To accelerate convergence toward the Pareto front and maintain a high degree of diversity for MaOPs, we propose a new many-objective dynamical evolutionary algorithm based on E-dominance and adaptive-grid strategies (EDAGEA). In EDAGEA, it incorporates the E_dominance and adaptive strategies to enhance the search ability. Instead of the Pareto dominance mechanism in the traditional dynamical evolutionary algorithm, EDAGEA employs the E-dominance strategy to improve the selective pressure and to accelerate the convergence speed. Moreover, EDAGEA incorporates the adaptive-grid strategy to promote the uniformity and diversity of the population. In the experiments, the proposed EDAGEA algorithm is tested on DTLZ series problems under 3–8 objectives with diverse characteristics and is compared with two excellent many-objective evolutionary algorithms. Experimental results demonstrate that the proposed EDAGEA algorithm exhibits competitive performance in terms of both convergence speed and diversity of population.

Yali Yin

Papers

Many-objective E-dominance dynamical evolutionary algorithm based on adaptive grid