In order to effectively solve the dynamic multi-objective optimization problem, a new dynamic multi-objective optimization algorithm based on prediction strategy is provided in this *** algorithm detects changes in th...
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In order to effectively solve the dynamic multi-objective optimization problem, a new dynamic multi-objective optimization algorithm based on prediction strategy is provided in this *** algorithm detects changes in the environment by recalculating individuals. At the same time, the prediction model is established based on individuals of the first two generations, which is used to generate the new individual. In order to improve the diversity, Cauchy Mutation is used in the algorithm. Then,there are the non-dominated sort and tournament selection to deal the individual. The proposed algorithm is validated on several typical test functions. Meanwhile, the algorithm is compared to DNSGA-A. The experimental results show that the rate of convergence gets improved and the population solution is closer to the real solution. The algorithm do well in resolving the basic dynamicmulti-objective function.
A dynamicmulti-objective genetic algorithm based on partial least squares prediction model (DNSGA-II-PLS) is presented in this paper to solve the mix traffic flow multi-objective timing optimization problem with time...
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A dynamicmulti-objective genetic algorithm based on partial least squares prediction model (DNSGA-II-PLS) is presented in this paper to solve the mix traffic flow multi-objective timing optimization problem with time-varying traffic demand. Take motor vehicle delay, non-motor vehicle delay, and pedestrian delay as objectives to solve the problem. Make comparison with three improved dynamicmulti-objective genetic algorithms based on prediction strategy: dynamicmulti-objective evolutionary algorithm based on simple prediction (DNSGA-II-PREM), autonomous regression dynamicmulti-objective evolutionary algorithm (DNSGA-II-AR), and mutation-based dynamicmulti-objective evolutionary algorithm (DNSGA-II-MUT) under four kinds of test functions. The results show that compared with the other three algorithms, the DNSGA-II-PLS algorithm proposed in this paper shows better performance in convergence and distribution, and this algorithm has less complexity. Finally, taking the intersections of Taiping North Road and Zhujiang Road in Nanjing as the research object, the performance of the algorithm is tested under the simulation environment. The results show that compared with the DNSGA-II-AR, which has best performance among three compared algorithms, the proposed DNSGA-II-PLS algorithm can effectively reduce the delay of motor vehicles by 6.7%, non-motor vehicles by -2.8%, and pedestrian waiting time by 20.5%.
In this paper, a novel dynamic multi-objective optimization algorithm is introduced. The proposed method is composed of three parts: change detection, response to change, and optimization process. The first step is to...
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In this paper, a novel dynamic multi-objective optimization algorithm is introduced. The proposed method is composed of three parts: change detection, response to change, and optimization process. The first step is to use Sentry solutions to detect the environmental change and advises the algorithm when a change occurs. Then, to increase the diversity of solutions, the worst solutions should be elected and removed from population and re-initialized with new solutions. The main idea is to use Borda count method which is an optimal rank aggregation technique that ranks the solutions in order of preference and nominates the worst solutions that should be removed. The last step is optimization process which is done by multi-objective Cat swarm optimization (CSO) in this paper. CSO utilizes the population that has been improved from the previous step to estimate the best solutions and converges to optimal Pareto front. The performance of the proposed algorithm is tested on dynamicmulti-objective benchmarks, and the results are compared with the ones achieved by previous algorithms. The simulation results indicate that the proposed algorithm can effectively track the time-varying optimal Pareto front and achieves competitive results in comparison with traditional approaches.
The importance of dynamic multi-objective optimization problems (DMOPs) is on the rise, in complex systems. DMOPs have several objective functions and constraints that vary over time to be considered simultaneously. A...
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ISBN:
(纸本)9781450376259
The importance of dynamic multi-objective optimization problems (DMOPs) is on the rise, in complex systems. DMOPs have several objective functions and constraints that vary over time to be considered simultaneously. As a result, the Pareto optimal solutions (POS) and Pareto front (PF) will also vary with time. The desired algorithm should not only locate the optima but also track the moving optima efficiently. In this paper, we propose a new Cultural Algorithm (CA) based on decomposition (CA/D). The primary objective of the CA/D algorithm is to decompose DMOP into several scalar optimization subproblems and solve simultaneously. The subproblems are optimized utilizing the information shared only by its neighboring problems. The proposed CA/D is evaluated using CEC 2015 optimization benchmark functions. The results show that CA/D outperforms CA, multi-population CA (MPCA), and MPCA incorporating game strategies (MPCA-GS), particularly in hybrid and composite benchmark problems.
Transfer learning has been used for solving multiple optimization and dynamic multi-objective optimization problems, since transfer learning is able to transfer useful information from one problem to help solving anot...
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ISBN:
(纸本)9781728124858
Transfer learning has been used for solving multiple optimization and dynamic multi-objective optimization problems, since transfer learning is able to transfer useful information from one problem to help solving another related problem. This paper aims to investigate when and how transfer learning works or fails in dynamic multi-objective optimization. Through computational analyses on a number of dynamic bi- and tri-objective benchmark problems, we show that transfer learning fails on problems with fixed Pareto optimal solution sets and under small environmental changes. We also show that the Gaussian kernel function used in the existing transfer learning-based method is not always adequate. Therefore, transfer learning should be avoided when dealing with problems for which transfer learning fails and other kernel functions should be used when the Gaussian kernel is inadequate. This paper proposes novel strategies and kernel functions that can be used in such cases. Experimental studies have demonstrated the superiority of our proposed techniques to state-of-the-art methods, on a number of dynamic bi- and tri-objective test problems.
dynamic multi-objective optimization problems (DMOPs) remain a challenge to be settled, because of conflicting objective functions change over time. In recent years, transfer learning has been proven to be a kind of e...
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ISBN:
(纸本)9781728124858
dynamic multi-objective optimization problems (DMOPs) remain a challenge to be settled, because of conflicting objective functions change over time. In recent years, transfer learning has been proven to be a kind of effective approach in solving DMOPs. In this paper, a novel transfer learning based dynamic multi-objective optimization algorithm (DMOA) is proposed called regression transfer learning prediction based DMOA (RTLP-DMOA). The algorithm aims to generate an excellent initial population to accelerate the evolutionary process and improve the evolutionary performance in solving DMOPs. When an environmental change is detected, a regression transfer learning prediction model is constructed by reusing the historical population, which can predict objective values. Then, with the assistance of this prediction model, some high-quality solutions with better predicted objective values are selected as the initial population, which can improve the performance of the evolutionary process. We compare the proposed algorithm with three state-of-the-art algorithms on benchmark functions. Experimental results indicate that the proposed algorithm can significantly enhance the performance of static multi-objectiveoptimization algorithms and is competitive in convergence and diversity.
This paper presents a novel dynamic multi-objective optimization algorithm based on region local search and memory（DMOA-RLSM）. Firstly, the NSGA2-DM stores useful information（memory） to guide population initializa...
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This paper presents a novel dynamic multi-objective optimization algorithm based on region local search and memory（DMOA-RLSM）. Firstly, the NSGA2-DM stores useful information（memory） to guide population initialization in the future;secondly, in the stage of population regeneration, DMOA-RLSM get corner points and sparse point according to the results of non-dominated sorting of current populations, define these points as the centers of border areas and sparse area respectively;thirdly, search around the corner points and sparse point ***-RLSM adopts extreme optimization strategy and random search strategy simultaneously to improve the quality of solutions and convergence rate. Performance of DMOA-RLSM is compared with two reported dynamic multi-objective optimization algorithms（DMOAs） for dMOP functions and FDA functions. Results show that the DMOA-RLSM performs better than the compared algorithms because of its low computational complexity.
The importance of dynamic multi-objective optimization problems (DMOPs) is on the rise, in complex systems. DMOPs have several objective functions and constraints that vary over time to be considered simultaneously. A...
详细信息
ISBN:
(纸本)9781450376259
The importance of dynamic multi-objective optimization problems (DMOPs) is on the rise, in complex systems. DMOPs have several objective functions and constraints that vary over time to be considered simultaneously. As a result, the Pareto optimal solutions (POS) and Pareto front (PF) will also vary with time. The desired algorithm should not only locate the optima but also track the moving optima efficiently. In this paper, we propose a new Cultural Algorithm (CA) based on decomposition (CA/D). The primary objective of the CA/D algorithm is to decompose DMOP into several scalar optimization subproblems and solve simultaneously. The subproblems are optimized utilizing the information shared only by its neighboring problems. The proposed CA/D is evaluated using CEC 2015 optimization benchmark functions. The results show that CA/D outperforms CA, multi-population CA (MPCA), and MPCA incorporating game strategies (MPCA-GS), particularly in hybrid and composite benchmark problems.
Many real-world multi-objectiveoptimization problems (MOPs) are dynamic in which variables of search space and/or objective space change over time. Hence the optimization algorithms should can quickly and efficiently...
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Many real-world multi-objectiveoptimization problems (MOPs) are dynamic in which variables of search space and/or objective space change over time. Hence the optimization algorithms should can quickly and efficiently track the Pareto front in dealing with dynamic environments. In this paper, a hybrid population prediction strategy based on fuzzy inference and one-step prediction (FIOPPS) is presented to extrapolate ahead the trajectory (position and/or orientation) of the new Pareto optimal solution set from the previous Pareto optimal solution sets and ensure the algorithm to respond quickly and effectively when the environment changes thus tracking the changing Pareto front. In our algorithm, the fuzzy inference model based on the Maximum Entropy Principle is extracted automatically from the previously found Pareto optimal solution sets to predict the Pareto solution sets at the beginning of the next time. Moreover, a new one-step prediction model is proposed to improve the prediction accuracy for environmental changes from motion state to static state and vice versa. Furthermore, a new variant of teaching-learning-based optimization algorithm with decomposition is first proposed as the MOEA optimizer for solving dynamic multi-objective optimization problems (DMOPs). In the proposed MOTLBO/D variant, the multi-objective decomposition mechanism is adopted and neighbor strategy is introduced into teaching-learning-based optimization algorithm (TLBO) to maintain the diversity of population and avoid the algorithm trapping into the local areas. Finally, to verify the performance of the proposed methods, ten benchmark test functions are simulated and evaluated. The statistical results indicate that the proposed FIOPPS strategy is promising for dealing with DMOPs.
dynamic multi-objective optimization problems (DMOPs) not only involve multiple conflicting objectives, but these objectives may also vary with time, raising a challenge for researchers to solve them. This paper prese...
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dynamic multi-objective optimization problems (DMOPs) not only involve multiple conflicting objectives, but these objectives may also vary with time, raising a challenge for researchers to solve them. This paper presents a cooperative co-evolutionary strategy based on environment sensitivities for solving DMOPs. In this strategy, a new method that groups decision variables is first proposed, in which all the decision variables are partitioned into two subcomponents according to their interrelation with environment. Adopting two populations to cooperatively optimize the two subcomponents, two prediction methods, i.e., differential prediction and Cauchy mutation, are then employed respectively to speed up their responses on the change of the environment. Furthermore, two improved dynamic multi-objective optimization algorithms, i.e., DNSGAII-CO and DMOPSO-CO, are proposed by incorporating the above strategy into NSGA-II and multi-objective particle swarm optimization, respectively. The proposed algorithms are compared with three state-of-the-art algorithms by applying to seven benchmark DMOPs. Experimental results reveal that the proposed algorithms significantly outperform the compared algorithms in terms of convergence and distribution on most DMOPs.
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