An Improved Multi-Objective Genetic Algorithm for Solving Multi-objective Problems

Hsieh, Sheng-Ta; Chiu, Shih-Yuan; Yen, Shi-Jim

doi:10.12785/amis/070531

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…The mine water reuse problem in this paper is summed up as a multi-objective optimization problem. Both particle swarm optimization (PSO) and genetic algorithm (GA) have great advantages and convenience for solving multi-objective optimization problems [ 43 , 44 , 45 , 46 ]. Therefore, this paper will make use of the respective advantages of particle swarm optimization and genetic algorithm to optimize the mine water distribution.…”

Section: Hybrid Improved Algorithm Based On Genetic Algorithms and Pa...mentioning

confidence: 99%

Economic Optimal Allocation of Mine Water Based on Two-Stage Adaptive Genetic Algorithm and Particle Swarm Optimization

Zhang

Yang

et al. 2022

Sensors

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The waste mine water is produced in the process of coal mining, which is the main cause of mine flood and environmental pollution. Therefore, economic treatment and efficient reuse of mine water is one of the main research directions in the mining area at present. It is an urgent problem to use an intelligent algorithm to realize optimal allocation and economic reuse of mine water. In order to solve this problem, this paper first designs a reuse mathematical model according to the mine water treatment system, which includes the mine water reuse rate, the reuse cost at different stages and the operational efficiency of the whole mine water treatment system. Then, a hybrid optimization algorithm, GAPSO, was proposed by combining genetic algorithm (GA) and particle swarm optimization (PSO), and adaptive improvement (TSA-GAPSO) was carried out for the two optimization stages. Finally, simulation analysis and actual data detection of the mine water reuse model are carried out by using four algorithms, respectively. The results show that the hybrid improved algorithm has better convergence speed and precision in solving the mine water scheduling problem. TSA-GAPSO algorithm has the best effect and is superior to the other three algorithms. The cost of mine water reuse is reduced by 9.09%, and the treatment efficiency of the whole system is improved by 5.81%, which proves the practicability and superiority of the algorithm.

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Section: Hybrid Improved Algorithm Based On Genetic Algorithms and Pa...mentioning

confidence: 99%

Economic Optimal Allocation of Mine Water Based on Two-Stage Adaptive Genetic Algorithm and Particle Swarm Optimization

Zhang

Yang

et al. 2022

Sensors

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show abstract

“…The specific steps of building dominated sets by exclusive method (Hsieh et al, 2013) are as follows:…”

Section: B P ′ =mentioning

confidence: 99%

Optimising hazardous materials transport network based on multi-objective hybrid intelligence algorithm

Xiang

2016

IJISTA

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Hazardous materials transport network optimisation is the basis of ensuring the safety of hazardous materials transport. Considering the uncertainty of much basic information in hazardous materials transport system, this paper proposes the multi-objective chance-constrained programming model for hazardous materials transport network under uncertainty optimisation theory framework. Then, the paper builds the multi-objective hybrid intelligent algorithm to solve the model. The algorithm applies the stochastic simulation and fuzzy simulation to simulate uncertain parameters, adopts the priority coding way to code chromosome, applies the chromosome marker selection strategy to complete the selection operation, adopts the priority index crossover operator to ensure offspring's inheritance and advantages for the parent, uses the single parent vicinal swap method to complete mutation and applies the exclusive method to build dominating sets. Finally, the case study shows the model and algorithm are feasible.

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Multi-objective optimization for modular granular neural networks applied to pattern recognition

Melín

Sánchez

2018

Information Sciences

102

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An Improved Multi-Objective Genetic Algorithm for Solving Multi-objective Problems

Cited by 4 publications

References 13 publications

Economic Optimal Allocation of Mine Water Based on Two-Stage Adaptive Genetic Algorithm and Particle Swarm Optimization

Economic Optimal Allocation of Mine Water Based on Two-Stage Adaptive Genetic Algorithm and Particle Swarm Optimization

Optimising hazardous materials transport network based on multi-objective hybrid intelligence algorithm

Multi-objective optimization for modular granular neural networks applied to pattern recognition

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