Cooperative Multiobjective Evolutionary Algorithm With Propulsive Population for Constrained Multiobjective Optimization

2021 ◽  
pp. 1-16
Author(s):  
Jiahai Wang ◽  
Yanyue Li ◽  
Qingfu Zhang ◽  
Zizhen Zhang ◽  
Shangce Gao
Keyword(s):  
Multiobjective Optimization ◽  
Evolutionary Algorithm ◽  
Multiobjective Evolutionary Algorithm ◽  
Constrained Multiobjective Optimization

Modeling the Multiobjective Optimization of Electricity Consumption for Residential Consumers

2013 ◽  
Vol 748 ◽  
pp. 493-497 ◽  
Author(s):  
José L. Bernal-Agustín ◽  
Tomás Cortés-Arcos ◽  
Rodolfo Dufo-López ◽  
Juan M. Lujano-Rojas ◽  
Cláudio Monteiro
Keyword(s):  
Mathematical Model ◽  
Multiobjective Optimization ◽  
Smart Grid ◽  
Evolutionary Algorithm ◽  
Pareto Optimality ◽  
Electricity Consumption ◽  
Cost Of Electricity ◽  
Multiobjective Evolutionary Algorithm ◽  
The Cost ◽  
The Mathematical Model

This paper presents a mathematical model to simultaneously optimize the cost of electricity and the satisfaction of a residential consumer using the communication infrastructure of a smart grid. For this task the concept of Pareto optimality has been used. It is possible to consider the satisfaction of the consumer as an independent objective to be maximized, and simultaneously, to minimize the cost of the electrical bill. In future works a multiobjective evolutionary algorithm will be applied along with the mathematical model presented in this paper.

scholarly journals A Novel Multiobjective Evolutionary Algorithm Based on Regression Analysis

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Zhiming Song ◽  
Maocai Wang ◽  
Guangming Dai ◽  
Massimiliano Vasile
Keyword(s):  
Regression Analysis ◽  
Probability Distribution ◽  
Multiobjective Optimization ◽  
Evolutionary Algorithm ◽  
Optimization Problem ◽  
Dimensional Manifold ◽  
Decision Space ◽  
Nsga Ii ◽  
Multiobjective Evolutionary Algorithm ◽  
Piecewise Continuous

As is known, the Pareto set of a continuous multiobjective optimization problem with m objective functions is a piecewise continuous (m-1)-dimensional manifold in the decision space under some mild conditions. However, how to utilize the regularity to design multiobjective optimization algorithms has become the research focus. In this paper, based on this regularity, a model-based multiobjective evolutionary algorithm with regression analysis (MMEA-RA) is put forward to solve continuous multiobjective optimization problems with variable linkages. In the algorithm, the optimization problem is modelled as a promising area in the decision space by a probability distribution, and the centroid of the probability distribution is (m-1)-dimensional piecewise continuous manifold. The least squares method is used to construct such a model. A selection strategy based on the nondominated sorting is used to choose the individuals to the next generation. The new algorithm is tested and compared with NSGA-II and RM-MEDA. The result shows that MMEA-RA outperforms RM-MEDA and NSGA-II on the test instances with variable linkages. At the same time, MMEA-RA has higher efficiency than the other two algorithms. A few shortcomings of MMEA-RA have also been identified and discussed in this paper.

scholarly journals A New Multiobjective Evolutionary Algorithm Based on Decomposition of the Objective Space for Multiobjective Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Cai Dai ◽  
Yuping Wang
Keyword(s):  
Multiobjective Optimization ◽  
Evolutionary Algorithm ◽  
Pareto Front ◽  
Optimization Problems ◽  
Experimental Studies ◽  
Evolutionary Process ◽  
Optimal Solution ◽  
Multiobjective Evolutionary Algorithm ◽  
Multiobjective Optimization Problems ◽  
Objective Space

In order to well maintain the diversity of obtained solutions, a new multiobjective evolutionary algorithm based on decomposition of the objective space for multiobjective optimization problems (MOPs) is designed. In order to achieve the goal, the objective space of a MOP is decomposed into a set of subobjective spaces by a set of direction vectors. In the evolutionary process, each subobjective space has a solution, even if it is not a Pareto optimal solution. In such a way, the diversity of obtained solutions can be maintained, which is critical for solving some MOPs. In addition, if a solution is dominated by other solutions, the solution can generate more new solutions than those solutions, which makes the solution of each subobjective space converge to the optimal solutions as far as possible. Experimental studies have been conducted to compare this proposed algorithm with classic MOEA/D and NSGAII. Simulation results on six multiobjective benchmark functions show that the proposed algorithm is able to obtain better diversity and more evenly distributed Pareto front than the other two algorithms.

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