scholarly journals Regional Integrated Energy Site Layout Optimization Based on Improved Artificial Immune Algorithm

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4381
Author(s):  
Yan Xu ◽  
Jianhao Zhang
Keyword(s):  
Decision Making ◽  
Load Capacity ◽  
Layout Optimization ◽  
Immune Algorithm ◽  
Initial Population ◽  
Artificial Immune ◽  
Weighting Method ◽  
Artificial Immune Algorithm ◽  
Multi Objective ◽  
Site Layout

Regional integrated energy site layout optimization involves multi-energy coupling, multi-data processing and multi-objective decision making, among other things. It is essentially a kind of non-convex multi-objective nonlinear programming problem, which is very difficult to solve by traditional methods. This paper proposes a decentralized optimization and comprehensive decision-making planning strategy and preprocesses the data information, so as to reduce the difficulty of solving the problem and improve operational efficiency. Three objective functions, namely the number of energy stations to be built, the coverage rate and the transmission load capacity of pipeline network, are constructed, normalized by linear weighting method, and solved by the improved p-median model to obtain the optimal value of comprehensive benefits. The artificial immune algorithm was improved from the three aspects of the initial population screening mechanism, population updating and bidirectional crossover-mutation, and its performance was preliminarily verified by test function. Finally, an improved artificial immune algorithm is used to solve and optimize the regional integrated energy site layout model. The results show that the strategies, models and methods presented in this paper are feasible and can meet the interest needs and planning objectives of different decision-makers.

A New Artificial Immune Algorithm for Flexible Job-Shop Scheduling

2010 ◽  
Vol 121-122 ◽  
pp. 266-270
Author(s):  
Lu Hong
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Immune Algorithm ◽  
Initial Population ◽  
Clonal Selection Algorithm ◽  
Scheduling Problem ◽  
Artificial Immune ◽  
Artificial Immune Algorithm ◽  
Shop Scheduling ◽  
Multi Objective

Flexible job-sop scheduling problem (FJSP) is based on the classical job-shop scheduling problem (JSP). however, it is even harder than JSP because of the addition of machine selection process in FJSP. An improved artificial immune algorithm, which combines the stretching technique and clonal selection algorithm is proposed to solve the FJSP. The algorithm can keep workload balance among the machines, improve the quality of the initial population and accelerate the speed of the algorithm’s convergence. The details of implementation for the multi-objective FJSP and the corresponding computational experiments are reported. The results indicate that the proposed algorithm is an efficient approach for the multi-objective FJSP.

Multi-objective artificial immune algorithm for fuzzy clustering based on multiple kernels

2019 ◽  
Vol 50 ◽  
pp. 100485 ◽  
Author(s):  
Ronghua Shang ◽  
Weitong Zhang ◽  
Feng Li ◽  
Licheng Jiao ◽  
Rustam Stolkin
Keyword(s):  
Fuzzy Clustering ◽  
Immune Algorithm ◽  
Artificial Immune ◽  
Artificial Immune Algorithm ◽  
Multi Objective

Mobile Robot Path Planning and Research in the Improved Artificial Immune Algorithm

2012 ◽  
Vol 466-467 ◽  
pp. 864-869 ◽  
Author(s):  
Yuan Bin Hou ◽  
Wei Wang ◽  
Xiao Yue Lu
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Optimal Path ◽  
Immune Algorithm ◽  
Initial Population ◽  
Robot Path Planning ◽  
Artificial Immune ◽  
Artificial Immune Algorithm ◽  
Selection Operator ◽  
Robot Path

Aim at local optimal problem in the path planning of mobile robot by artificial immune algorithm, it is proposed that the improved artificial immune algorithm of mobile robot path planning. Based on artificial immunity algorithm, the potential function method of an artificial potential field is used in this algorithm, improving randomness of the initial population of the artificial immune algorithm, then the algorithm make initial population turn to evolutionary operation through crossover, variance and selection operator to get optimum antibody. The simulation results showed that this algorithm is easy to get the optimal path, at the same time, increasing the speed of the path planning, and the length of the optimal path planning is less 28.5% compare with the traditional immune algorithm.

An Artificial Immune Algorithm and its Application in AVO Inversion

2012 ◽  
Vol 268-270 ◽  
pp. 1779-1782
Author(s):  
Hai Yan Zhang ◽  
Zi Li Liu
Keyword(s):  
Global Optimization ◽  
Wave Amplitude ◽  
Immune Algorithm ◽  
P Wave ◽  
Initial Population ◽  
Artificial Immune ◽  
Amplitude Variation ◽  
Artificial Immune Algorithm ◽  
Amplitude Variation With Offset ◽  
Avo Inversion

An improved artificial immune algorithm is proposed for geophysical P-wave amplitude variation with offset (AVO) inversion. In this paper, the algorithm is described and implemented. The orthogonal crossover is used to generate the initial population and the elitist-crossover is adopted to add the good patterns of the population. The hybrid mutation method is presented to increase the ability of local and global optimization. The improved immune algorithm is then applied to earth interface models of Mexican gulf for AVO inversion. The experimental results show that the improved algorithm is of high precision than the traditional immune algorithm.

Application of Multi-Objective Artificial Immune Optimal Algorithm in the Elevator Group Control System

2011 ◽  
Vol 130-134 ◽  
pp. 3557-3561
Author(s):  
Yue Min Liu ◽  
Yan Zhu
Keyword(s):  
Control System ◽  
Service Quality ◽  
Hybrid Algorithm ◽  
Immune Algorithm ◽  
Artificial Immune ◽  
Artificial Immune Algorithm ◽  
Multi Objective ◽  
Group Control ◽  
Elevator Group Control ◽  
Weighted Values

With the development of the electronic technology, people have proposed higher requirements for the service quality on elevator, and the optimal elevator dispatching has developed a typical multi-objective optimal process. This paper analyzes both the advantages and the disadvantages of artificial immune algorithm and gradient descent algorithm, optimizes artificial immune algorithm, then proposes a novel optimal hybrid algorithm; at the same time, uses this hybrid algorithm in the elevator group control system combined with Pareto solution set. Making a comparison between the hybrid algorithm and the standard artificial immune algorithm, it’s clear that this hybrid algorithm has certain feasibility and superiority, and to some extent, has improved the overall performance and service quality of the elevator group control system. This paper has provided a new method and a new thought on determination of the multi-objective weighted values in the elevator group control system.

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