A similarity-based cooperative co-evolutionary algorithm for dynamic interval multi-objective optimization problems

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Dynamic interval multi-objective optimization problems (DI-MOPs) are very common in real-world applications. However, there are few evolutionary algorithms that are suitable for tackling DI-MOPs up to date. A framework of dynamic interval multi-objective cooperative co-evolutionary optimization based on the interval similarity is presented in this paper to handle DI-MOPs. In the framework, a strategy for decomposing decision variables is first proposed, through which all the decision variables are divided into two groups according to the interval similarity between each decision variable and interval parameters. Following that, two sub-populations are utilized to cooperatively optimize decision variables in the two groups. Furthermore, two response strategies, rgb0.00,0.00,0.00i.e., a strategy based on the change intensity and a random mutation strategy, are employed to rapidly track the changing Pareto front of the optimization problem. The proposed algorithm is applied to eight benchmark optimization instances rgb0.00,0.00,0.00as well as a multi-period portfolio selection problem and compared with five state-of-the-art evolutionary algorithms. The experimental results reveal that the proposed algorithm is very competitive on most optimization instances

Process Knowledge-guided Autonomous Evolutionary Optimization for Constrained Multiobjective Problems

Various real-world problems can be attributed to constrained multi-objective optimization problems. Although there are various solution methods, it is still very challenging to automatically select efficient solving strategies for constrained multi-objective optimization problems. Given this, a process knowledge-guided constrained multi-objective autonomous evolutionary optimization method is proposed. Firstly, the effects of different solving strategies on population states are evaluated in the early evolutionary stage. Then, the mapping model of population states and solving strategies is established. Finally, the model recommends subsequent solving strategies based on the current population state. This method can be embedded into existing evolutionary algorithms, which can improve their performances to different degrees. The proposed method is applied to 41 benchmarks and 30 dispatch optimization problems of the integrated coal mine energy system. Experimental results verify the effectiveness and superiority of the proposed method in solving constrained multi-objective optimization problems.The National Key R&D Program of China, the National Natural Science Foundation of China, Shandong Provincial Natural Science Foundation, Fundamental Research Funds for the Central Universities and the Open Research Project of The Hubei Key Laboratory of Intelligent Geo-Information Processing.http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4235hj2023Electrical, Electronic and Computer Engineerin

How good are distributed allocation algorithms for solving urban search and rescue problems? A comparative study with centralized algorithms

In this paper, a modified centralized algorithm based on particle swarm optimization (MCPSO) is presented to solve the task allocation problem in the search and rescue domain. The reason for this paper is to provide a benchmark against distributed algorithms in search and rescue application area. The hypothesis of this paper is that a centralized algorithm should perform better than distributed algorithms because it has all the available information at hand to solve the problem. Therefore, the centralized approach will provide a benchmark for evaluating how well the distributed algorithms are working and how much improvement can still be gained. Among the distributed algorithms, the consensus-based bundle algorithm (CBBA) is a relatively recent method based on the market auction mechanism, which is receiving considerable attention. Other distributed algorithms, such as PI and PI with softmax, have shown to perform better than CBBA. Therefore, in this paper, the three distributed algorithms mentioned earlier are compared against three centralized algorithms. They are particle swarm optimization, MCPSO, described in this paper, and genetic algorithms. Two experiments were conducted. The first involved comparing all the above-mentioned algorithms, both centralized and distributed, using the same set of application scenarios. It is found that MCPSO always outperforms the other five algorithms in time cost. Due to the high failure rate of CBBA and the other two centralized methods, the second experiment focused on carrying out more tests to compare MCPSO against PI and PI with softmax. All the results are shown and analyzed to determine the performance gaps between the distributed algorithms and the MCPSO

A feedback-directed method of evolutionary test data generation for parallel programs

Context: Genetic algorithms can be utilized for automatic test data generation. Test data are encoded as individuals which are evolved for a number of generations using genetic operators. Test data of a parallel program include not only the program input, but also the communication information between each pair of processes. Traditional genetic algorithms, however, do not make full use of information provided by a population’s evolution, resulting in a low efficiency in generating test data. Objective: This paper emphasizes the problem of test data generation for parallel programs, and presents a feedback-directed genetic algorithm for generating test data of path coverage. Method: Information related to a schedule sequence is exploited to improve genetic operators. Specifically, a scheduling sequence is evaluated according to how well an individual covers the target path. The probability of the crossover and mutation points being located in the region is determined based on the evaluation result, which prevents a good schedule sequence from being destroyed. If crossover and mutation are performed in the scheduling sequence, the location of crossover and mutation points is further determined according to the relationship between nodes to be covered and the scheduling sequence. In this way, the population can be evolved in a narrowed search space. Results: The proposed algorithm is applied to test 11 parallel programs. The experimental results show that, compared with the genetic algorithm without utilizing information during the population evolution, the proposed algorithm significantly reduces the number of generations and the time consumption. Conclusion: The proposed algorithm can greatly improve the efficiency in evolutionary test data generation

An environment-driven hybrid evolutionary algorithm for dynamic multi-objective optimization problems

open access articleIn dynamic multi-objective optimization problems, the environmental parameters may change over time, which makes the Pareto fronts shifting. To address the issue, a common idea is to track the moving Pareto front once an environmental change occurs. However, it might be hard to obtain the Pareto optimal solutions if the environment changes rapidly. Moreover, it may be costly to implement a new solution. By contrast, robust Pareto optimization over time provides a novel framework to find the robust solutions whose performance is acceptable for more than one environment, which not only saves the computational costs for tracking solutions, but also minimizes the cost for switching solutions. However, neither of the above two approaches can balance between the quality of the obtained non-dominated solutions and the computation cost. To address this issue, environment-driven hybrid dynamic multi-objective evolutionary optimization method is proposed, aiming to fully use strengths of TMO and RPOOT under various characteristics of environmental changes. Two indexes, i.e., the frequency and intensity of environmental changes, are first defined. Then, a criterion is presented based on the characteristics of dynamic environments and the switching cost of solutions, to select an appropriate optimization method in a given environment. The experimental results on a set of dynamic benchmark functions indicate that the proposed hybrid dynamic multi-objective evolutionary optimization method can choose the most rational method that meets the requirements of decision makers, and balance the convergence and robustness of the obtained non-dominated solutions

Genetic Algorithm-Based Test Data Generation for Multiple Paths via Individual Sharing

The application of genetic algorithms in automatically generating test data has aroused broad concerns and obtained delightful achievements in recent years. However, the efficiency of genetic algorithm-based test data generation for path testing needs to be further improved. In this paper, we establish a mathematical model of generating test data for multiple paths coverage. Then, a multipopulation genetic algorithm with individual sharing is presented to solve the established model. We not only analyzed the performance of the proposed method theoretically, but also applied it to various programs under test. The experimental results show that the proposed method can improve the efficiency of generating test data for many paths’ coverage significantly