Multi-objective Shuffled Frog-leaping Algorithm Research Articles

A multi-objective memetic algorithm for query-oriented text summarization: Medicine texts as a case study

Automatic text summarization is a topic of great interest in many fields of knowledge. Particularly, query-oriented extractive multi-document text summarization methods have increased their importance recently, since they can automatically generate a summary according to a query given by the user. One way to address this problem is by multi-objective optimization approaches. In this paper, a memetic algorithm, specifically a Multi-Objective Shuffled Frog-Leaping Algorithm (MOSFLA) has been developed, implemented, and applied to solve the query-oriented extractive multi-document text summarization problem. Experiments have been conducted with datasets from Text Analysis Conference (TAC), and the obtained results have been evaluated with Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics. The results have shown that the proposed approach has achieved important improvements with respect to the works of scientific literature. Specifically, 25.41%, 7.13%, and 30.22% of percentage improvements in ROUGE-1, ROUGE-2, and ROUGE-SU4 scores have been respectively reached. In addition, MOSFLA has been applied to medicine texts from the Topically Diverse Query Focus Summarization (TD-QFS) dataset as a case study.

A novel spatial optimization approach for the cost-effectiveness improvement of LID practices based on SWMM-FTC

Due to the increasingly frequent occurrence of urban waterlogging, the spatial optimization of low impact development (LID) practices has been commonly used to detain and reduce storm water runoff in the most cost-effective way. In this study, the flow transmission chain (FTC) was proposed to replace the routing portion of the Storm Water Management Model (SWMM) and was combined with the runoff component of the SWMM to simulate LID practices (SWMM-FTC). In the SWMM-FTC, the third Evolution Step of Generalized Differential Evolution (GDE3) was employed to optimize the LID layout design. The results showed that the relative error between the modified SWMM-FTC and the calibrated SWMM was less than 0.25% under various LID scenarios, and the computational efficiency of the SWMM-FTC was improved by 19.3 times. Moreover, the GDE3 outperformed the commonly used non-dominated sorting genetic algorithm (NSGA-II), the strength Pareto evolutionary algorithm (SPEA2), and the multi-objective shuffled frog leaping algorithm (MOSFLA) due to its ability to find the most cost-effective solution. The LID layout obtained from the SWMM-FTC with the GDE3 saved $210–1067 to achieve a 1% reduction in storm water runoff. This result demonstrates that the SWMM-FTC with the GDE3 can achieve higher environmental benefits than comparable models, providing better guidance for managers and stakeholders.

A hybrid algorithm based on MOSFLA and GA for multi-UAVs plant protection task assignment and sequencing optimization

We propose a Multi-Objective Shuffled Frog-Leaping Algorithm (MOSFLA) and Genetic Algorithm (GA) based task assignment and sequencing method, for multi-Unmanned Aerial Vehicles (multi-UAVs) plant-protection operation optimization. Based on full coverage of spray area, a dual decision model of non-operation flight distance and total operation time is developed considering energy consumption and operation efficiency. The proposed optimization method is hybridized using MOSFLA and GA: we first use modified MOSFLA for multi-UAVs operation assignment optimization, shrinking multi-UAVs operation cost including fields allocation, non-operation flight distance and operation time difference; we then employ GA for fields operation sequencing optimization, reducing the total operation time. Considering multi-UAVs’ take-off preparation delay effect, we established a latency time calculation model to determine total operation time for multi-UAVs. The test results show that: ① the non-operation flight distance cost for multi-UAVs using MOSFLA is less than that of single-UAV, which also performs better than that for multi-UAVs in traditional modes; ② the total operation time shrinks by using GA with known assignment matrix (including MOSFLA), which could save over 20 min compared with traditional modes; ③ the total operation time cost in MOSFLA–GA is less than that in traditional modes and optimized traditional modes, with same UAV number and preparation time for take-off; ④ the minimum iteration when assignment fitness value of MOSFLA attains maximum is small (< 20); the minimum iteration when sequencing fitness value of GA attains maximum is less than 100, but is 5 in certain cases.

A fast and robust simulation-optimization methodology for stormwater quality management

The simulation and optimization of low impact development (LID) practices has been a key research topic in stormwater management. In this study, a fast and robust framework was proposed for providing the optimal design of LID practices by coupling a physically based model, the Markov chain, with the multi-objective shuffled frog leaping algorithm (MOSFLA). The proposed framework was then tested for chemical oxygen demand (COD) reduction in a typical urban catchment in China. The storm water management model (SWMM) was used to provide the flow/COD data during the baseline scenario, and the Markov chain method was then incorporated as a subset of the physically based model. Based on the results, the computational efficiency was improved by 500 times when the new framework was used, and the robustness of the optimal results increased over 50% compared to commonly used algorithms. The relative error between the SWMM and the Markov chain method was less than 5%, indicating that a satisfactory performance could be obtained using the proposed framework. This new method provides a useful tool for optimizing LID practices and green infrastructure, especially for complex urban catchments.

Multi-objective memetic meta-heuristic algorithm for encoding the same protein with multiple genes

An important goal in synthetic biology is to maximize the expression levels of proteins. For this purpose, multiple genes encoding the same protein can be integrated into the host genome. However, this approach is affected by two key issues. Firstly, codons with better adaptation indexes should be used, since some synonymous codons are better adapted than others. Secondly, the multiple protein-coding sequences should be as different as possible to avoid the loss of gene copies due to homologous recombination. Therefore, this task shows strict biological requirements that make it difficult to tackle. In this work, we design and implement a computational intelligence approach to address this problem, the Multi-Objective Shuffled Frog Leaping Algorithm (MOSFLA). This method combines the optimization capabilities provided by parallel searches, multiple operators, and memetic strategies to tackle problems with difficult solution quality requirements. Several alternatives have been comparatively analyzed, including MOSFLA variants with three objectives as in other approaches from the literature and also variants with only two objectives. Experiments on nine real-world protein datasets give account of the improved, statistically significant performance achieved over the related work, attending to different quality metrics, confirming that our proposal satisfactorily deals with the complex nature of the problem.

A multiobjective adaptive approach for the inference of evolutionary relationships in protein-based scenarios

Complex optimization problem solving is a constant issue in a wide range of scientific domains. Robust bioinspired procedures with accurate search capabilities are therefore required to address the challenge that such optimization problems represent. This work explores different design alternatives for the metaheuristic Multiobjective Shuffled Frog-Leaping Algorithm, a novel method that combines parallel searches and swarm-based operators to undertake the processing of complex search spaces. Three variants of the metaheuristic are adopted: a dominance-based approach, an indicator-based alternative, and an adaptive proposal that incorporates both multiobjective strategies (dynamically assigning during the execution more resources to the most successful strategy). The performance of the proposed designs is examined when tackling, as a case study, the inference of ancestral relationships from protein data, using different multiobjective metrics and bio-statistical testing procedures. Experimental results show the additional robustness that the adaptive technique provides to the metaheuristic, allowing its search engine to exploit the most fitting multiobjective approach according to the status of the optimization process.

Multi-Objective Cluster Intelligent Algorithms for Railway Door-to-Door Transportation Routing Design

In this study, we aim to develop a system optimization model of Railway Freight Transportation Routing Design (RFTRD) and conduct solution analysis which is based on the improved multi-objective swarm intelligence algorithm. The proposed improved multi-objective swarm intelligence algorithm is applied to solve the combinatorial optimization problem of railway door-to-door freight transportation through design, and provide decision support for railway vehicle door-to-door freight transportation through design. The optimization results shows that, the random multi-neighborhood based multi-objective shuffled frog-leaping algorithm with path relinking (RMN-MOSFLA-PR) can be better applied to solve the combined multi-objective optimization problem, and this proposed improved algorithm can find Pareto frontier through the comparative analysis in the design example of railway door-to-door freight transportation. The frontier can provide support for railway transportation enterprises, arrange the decision-making of the starting and ending stations for multiple shippers, and optimize the use of existing transportation resources, so as to reduce the transportation cost and time of the system.

A shuffled frog-leaping algorithm based mixed-sensitivity H∞ control of a seismically excited structural building using MR dampers

An intelligent robust controller, which combines a shuffled frog-leaping algorithm (SFLA) and an H∞ control strategy, is designed for a semi-active control system with magnetorheological (MR) dampers to reduce seismic responses of structures. Generally, the performance of mixed-sensitivity H∞ (MSH) control highly depends on expert experience in selecting the parameters of the weighting functions. In this study, as a recently-developed heuristic approach, a multi-objective SFLA with constraints is adopted to search for the optimal weighting functions. In the proposed semi-active control, firstly, based on the Bouc–Wen model, the forward dynamic characteristics of the MR damper are investigated through a series of tensile and compression experiments. Secondly, the MR damper inverse model is developed with an adaptive-network-based fuzzy inference system (ANFIS) technique. Finally, the SFLA-optimized MSH control approach integrated with the ANFIS inverse model is used to suppress the structural vibration. The simulation results for a three-story building model equipped with an MR damper verify that the proposed semi-active control method outperforms fuzzy control and two passive control methods. Besides, with the proposed strategy, the changes in structural parameters and earthquake excitations can be satisfactorily dealt with.

Novel Multiobjective Shuffled Frog Leaping Algorithm with Application to Reservoir Flood Control Operation

Reservoir flood control operation (RFCO) is a large scale multiobjective problem with complex constraints that require powerful algorithms to solve it. As a new metaheuristic evolutionary algorithm, shuffled frog leaping algorithm (SFLA) has the potential ability to solve multiobjective optimization problems because of its group evolution characteristic. In this paper, we present a novel multiobjective shuffled frog leaping algorithm (MOSFLA), which incorporates an archiving strategy based on self-adaptive niche method to maintain the nondominated solutions, and improves the memetic evolution process of SFLA to adapt to the multiobjective optimization problem. The numerical experiments of five Zitzler-Deb-Thiele functions indicate that MOSFLA yields better-spread solutions and converges closer to the true Pareto frontier than non-denominated sorting genetic algorithm (NGSA)-II and SPEA2. Furthermore, MOSFLA is applied to solve RFCO of the Three Gorges Project, and the results demonstrate that this algorithm can generate a solution set with uniform spread and good convergence for the problems with two conflicting objectives, including minimizing the highest reservoir water level and minimizing the peak flood discharge. Additionally, if compared with dynamic programming and NGSA-II, MOSFLA is verified to be more efficient and competitive, and thus can be provided as a new effective alternative for solving the complex reservoir operation problems.

A novel hybrid multi-objective shuffled frog-leaping algorithm for a bi-criteria permutation flow shop scheduling problem

This paper investigates a novel multi-objective model for a permutation flow shop scheduling problem that minimizes both the weighted mean earliness and the weighted mean tardiness. Since a flow shop scheduling problem has been proved to be NP-hard in a strong sense, a new hybrid multi-objective algorithm based on shuffled frog-leaping algorithm (SFLA) and variable neighborhood search (VNS) is devised to find Pareto optimal solutions for the given problem. To validate the performance of the proposed hybrid multi-objective shuffled frog-leaping algorithm (HMOSFLA) in terms of solution quality and diversity level, various test problems are examined. Further, the efficiency of the proposed algorithm, based on various salient metrics, is compared against two well-known multi-objective genetic algorithms: NSGA-II and SPEA-II. Our computational results suggest that the proposed HMOSFLA outperforms the two foregoing algorithms, especially for large-sized problems.

A hybrid multi-objective shuffled frog-leaping algorithm for a mixed-model assembly line sequencing problem

In this paper, a mixed-model assembly line (MMAL) sequencing problem is studied. This type of production system is used to manufacture multiple products along a single assembly line while maintaining the least possible inventories. With the growth in customers’ demand diversification, mixed-model assembly lines have gained increasing importance in the field of management. Among the available criteria used to judge a sequence in MMAL, the following three are taken into account: the minimization of total utility work, total production rate variation, and total setup cost. Due to the complexity of the problem, it is very difficult to obtain optimum solution for this kind of problems by means of traditional approaches. Therefore, a hybrid multi-objective algorithm based on shuffled frog-leaping algorithm (SFLA) and bacteria optimization (BO) are deployed. The performance of the proposed hybrid algorithm is then compared with three well-known genetic algorithms, i.e. PS-NC GA, NSGA-II, and SPEA-II. The computational results show that the proposed hybrid algorithm outperforms the existing genetic algorithms, significantly in large-sized problems.

Solving a bi-criteria permutation flow-shop problem using shuffled frog-leaping algorithm

Flow shop problems as a typical manufacturing challenge have gained wide attention in academic fields. In this paper, we consider a bi-criteria permutation flow shop scheduling problem, where the weighted mean completion time and the weighted mean tardiness are to be minimized simultaneously. Due to the complexity of the problem, it is very difficult to obtain optimum solution for this kind of problems by means of traditional approaches. Therefore, a new multi-objective shuffled frog-leaping algorithm (MOSFLA) is introduced for the first time to search locally Pareto-optimal frontier for the given problem. To prove the efficiency of the proposed algorithm, various test problems are solved and the reliability of the proposed algorithm, based on some comparison metrics, is compared with three distinguished multi-objective genetic algorithms, i.e. PS-NC GA, NSGA-II, and SPEA-II. The computational results show that the proposed MOSFLA performs better than the above genetic algorithms, especially for the large-sized problems.