Multi-objective adaptive Clonal selection algorithm for solving environmental/economic dispatch and OPF problems with load uncertainty

In order to solve the conflict multi-objective optimization of truss structures between the structure minimum weight and safety redundancy, the immune clonal selection algorithm based on information entropy was adopted in this paper. Based on the immunology theory, the non-dominated neighbor-based selection, proportional cloning and elitism strategy were introduced in the multi-objective immune clonal selection algorithm (MOICSA) to enhance the diversity, the uniformity and the convergence of the obtained solution. Mathematical models for truss multi-objective optimization design are constructed, in which the information entropy value of bar stress is taken as one of objective functions, and penalty function method was used to deal with violated constraints. Several classical problems are solved using the MOICSA algorithm, and the results compared with other optimization methods. The simulation results show that the method can achieve the effect of multiple-objective optimization successfully.

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An improved immune clonal selection algorithm for bi-objective robotic assemble line balancing problems considering time and space constraints

Engineering Computations ◽

10.1108/ec-11-2018-0512 ◽

2019 ◽

Vol 36 (6) ◽

pp. 1868-1892 ◽

Cited By ~ 2

Author(s):

Binghai Zhou ◽

Qiong Wu

Keyword(s):

Assembly Line ◽

Clonal Selection ◽

Assembly Line Balancing ◽

Robotic Assembly ◽

Line Balancing ◽

Clonal Selection Algorithm ◽

Selection Algorithm ◽

Assembly Lines ◽

Content Type ◽

Multi Objective

Purpose The extensive applications of the industrial robots have made the optimization of assembly lines more complicated. The purpose of this paper is to develop a balancing method of both workstation time and station area to improve the efficiency and productivity of the robotic assembly lines. A tradeoff was made between two conflicting objective functions, minimizing the number of workstations and minimizing the area of each workstation. Design/methodology/approach This research proposes an optimal method for balancing robotic assembly lines with space consideration and reducing robot changeover and area for tools and fixtures to further minimize assembly line area and cycle time. Due to the NP-hard nature of the considered problem, an improved multi-objective immune clonal selection algorithm is proposed to solve this constrained multi-objective optimization problem, and a special coding scheme is designed for the problem. To enhance the performance of the algorithm, several strategies including elite strategy and global search are introduced. Findings A set of instances of different problem scales are optimized and the results are compared with two other high-performing multi-objective algorithms to evaluate the efficiency and superiority of the proposed algorithm. It is found that the proposed method can efficiently solve the real-world size case of time and space robotic assembly line balancing problems. Originality/value For the first time in the robotic assembly line balancing problems, an assignment-based tool area and a sequence-based changeover time are took into consideration. Furthermore, a mathematical model with bi-objective functions of minimizing the number of workstations and area of each station was developed. To solve the proposed problem, an improved multi-objective immune clonal selection algorithm was proposed and a special coding scheme is designed.

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