scholarly journals Weapon-Target Assignment Problem Using Modified Water Wave Optimization Algorithm

2021 ◽  
Vol 2068 (1) ◽  
pp. 012017
Author(s):  
Yuanfei Wei ◽  
Qifang Luo ◽  
Yongquan Zhou
Keyword(s):  
Mathematical Expectation ◽  
Water Wave ◽  
Local Optimum ◽  
Search Performance ◽  
Decision Matrix ◽  
Classic Problem ◽  
Target Assignment ◽  
Search Speed ◽  
Overall Performance ◽  
Optimized Allocation

Abstract The weapon-target assignment (WTA) is a classic problem. The WTA mathematical model is that warship formations are reasonably equipped with weapons resources for each weapon system to attack the air threaten targets. The purpose of targets optimization is to maximize combat effectiveness, that is to say, the mathematical expectation is maximum. We adopt the greedy strategy and improved propagation operation is to strengthen the water wave optimization (WWO) search performance. This article elaborates a modified water wave optimization (MWWO) to solve the WTA problem, which can detect optimized allocation decision matrix and search for the maximum mathematical expectation. Based on parameter optimization, the overall performance of the MWWO is more stable, the search speed is accelerated and the accuracy is improved. The experiment results indicate that the MWWO are verified and avoids local optimum, and can be more convenient for solving the WTA and obtain better performance.

scholarly journals Dialogue Generation: From Imitation Learning to Inverse Reinforcement Learning

2019 ◽  
Vol 33 ◽  
pp. 6722-6729 ◽  
Author(s):  
Ziming Li ◽  
Julia Kiseleva ◽  
Maarten De Rijke
Keyword(s):  
Reinforcement Learning ◽  
State Of The Art ◽  
The State ◽  
Experimental Results ◽  
Imitation Learning ◽  
Local Optimum ◽  
Inverse Reinforcement Learning ◽  
High Quality ◽  
Overall Performance

The performance of adversarial dialogue generation models relies on the quality of the reward signal produced by the discriminator. The reward signal from a poor discriminator can be very sparse and unstable, which may lead the generator to fall into a local optimum or to produce nonsense replies. To alleviate the first problem, we first extend a recently proposed adversarial dialogue generation method to an adversarial imitation learning solution. Then, in the framework of adversarial inverse reinforcement learning, we propose a new reward model for dialogue generation that can provide a more accurate and precise reward signal for generator training. We evaluate the performance of the resulting model with automatic metrics and human evaluations in two annotation settings. Our experimental results demonstrate that our model can generate more high-quality responses and achieve higher overall performance than the state-of-the-art.

scholarly journals The effects of induced optical blur on visual search performance and training

2021 ◽  
pp. 174702182110502
Author(s):  
Azuwan Musa ◽  
Alison R Lane ◽  
Amanda Ellison
Keyword(s):  
Visual Acuity ◽  
Visual Search ◽  
Visual Field Loss ◽  
Search Performance ◽  
Searching Behaviour ◽  
Feature Search ◽  
Search Tasks ◽  
Search Speed ◽  
Search Training ◽  
Optical Blur

Visual search is a task often used in the rehabilitation of patients with cortical and non-cortical visual pathologies such as visual field loss. Reduced visual acuity is often comorbid with these disorders, and it remains poorly defined how low visual acuity may affect a patient’s ability to recover visual function through visual search training. The two experiments reported here investigated whether induced blurring of vision (from 6/15 to 6/60) in a neurotypical population differentially affected various types of feature search tasks, whether there is a minimal acceptable level of visual acuity required for normal search performance, and whether these factors affected the degree to which participants could improve with training. From the results, it can be seen that reducing visual acuity did reduce search speed, but only for tasks where the target was defined by shape or size (not colour), and only when acuity was worse than 6/15. Furthermore, searching behaviour was seen to improve with training in all three feature search tasks, irrespective of the degree of blurring that was induced. The improvement also generalised to a non-trained search task, indicating that an enhanced search strategy had been developed. These findings have important implications for the use of visual search as a rehabilitation aid for partial visual loss, indicating that individuals with even severe comorbid blurring should still be able to benefit from such training.

Test delle matrici: il rapporto target/distrattori e il ruolo della memoria a breve termine

2009 ◽  
pp. 29-46
Author(s):  
Claudio Luzzatti ◽  
Carlo Abbate ◽  
Carlo Vergani
Keyword(s):  
Visual Search ◽  
Short Term Memory ◽  
Search Performance ◽  
Accuracy Score ◽  
Performance Score ◽  
Short Term ◽  
Term Memory ◽  
Span Test ◽  
Significant Difference ◽  
Overall Performance

- We studied the effect of the target-to-distractor ratio (T/D) and short term memory on a matrix test performance. Higher performance on a visual search test with rising T/D ratio was found. An overall performance score improvement from the first to the third matrix is expected, because of the T/D ratio increase. In a previous study we found a significant difference on accuracy scores between the first and the following matrixes. In this article we demonstrate that an involvement of the short term memory processes could account for this result. Two hundred and twenty seven subjects from the Geriatric Unit of Ospedale Policlinico of Milan were considered. The subjects were 159 female and 68 male, aged 58 to 94 years with 3 to 18 years of education. Patients suffering from acute or chronic neurological diseases, sensorial impairment and alcoholism were excluded. We examined retrospectively the performance obtained by the subjects on a matrix test and a bisyllabic words span test. Correlation between accuracy scores obtained for the different matrixes of the attention test and the span score was then calculated. We found: 1) a significant difference on overall performance score between the three matrixes of the attention test: scores increase with the rise of T/D ratio; 2) a significant correlation between the accuracy score of the second and third matrix and the score of the span test; 3) no correlation between accuracy scores on the first matrix and the short-term memory score. In conclusion our data confirm the positive effect of a larger target-to-distractor ratio on the visual search performance in elderly people. The hypothesis that short-term memory is involved in the execution of the second and third matrix is preliminarly confirmed. The results are discussed in terms of the signal detection theory.

Quotient Space-Based Boundary Condition for Particle Swarm Optimization Algorithm

2013 ◽  
pp. 31-42
Author(s):  
Yuhong Chi ◽  
Fuchun Sun ◽  
Langfan Jiang ◽  
Chunyang Yu ◽  
Chunli Chen
Keyword(s):  
Boundary Condition ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Quotient Space ◽  
Particle Swarm ◽  
Search Space ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Search Speed

To control particles to fly inside the limited search space and deal with the problems of slow search speed and premature convergence of particle swarm optimization algorithm, this paper applies the theory of topology, and proposed a quotient space-based boundary condition named QsaBC by using the properties of quotient space and homeomorphism. In QsaBC, Search space-zoomed factor and Attractor are introduced according to the dynamic behavior and stability of particles, which not only reduce the subjective interference and enforce the capability of global search, but also enhance the power of local search and escaping from an inferior local optimum. Four CEC’2008 benchmark functions are selected to evaluate the performance of QsaBC. Comparative experiments show that QsaBC can achieve the satisfactory optimization solution with fast convergence speed. Furthermore, QsaBC is more effective with errant particles, and has easier calculation and better robustness than other methods.

scholarly journals Overall Performance Evaluation of Tubular Scraper Conveyors Using a TOPSIS-Based Multiattribute Decision-Making Method

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Yanping Yao ◽  
Ziming Kou ◽  
Wenjun Meng ◽  
Gang Han
Keyword(s):  
Optimal Solution ◽  
Ideal Solution ◽  
Evaluation Tool ◽  
Decision Matrix ◽  
Measurement Results ◽  
Optimal Deployment ◽  
Overall Performance ◽  
Space Material ◽  
Negative Ideal Solution ◽  
Filling Coefficient

Properly evaluating the overall performance of tubular scraper conveyors (TSCs) can increase their overall efficiency and reduce economic investments, but such methods have rarely been studied. This study evaluated the overall performance of TSCs based on the technique for order of preference by similarity to ideal solution (TOPSIS). Three conveyors of the same type produced in the same factory were investigated. Their scraper space, material filling coefficient, and vibration coefficient of the traction components were evaluated. A mathematical model of the multiattribute decision matrix was constructed; a weighted judgment matrix was obtained using the DELPHI method. The linguistic positive-ideal solution (LPIS), the linguistic negative-ideal solution (LNIS), and the distance from each solution to the LPIS and the LNIS, that is, the approximation degrees, were calculated. The optimal solution was determined by ordering the approximation degrees for each solution. The TOPSIS-based results were compared with the measurement results provided by the manufacturer. The ordering result based on the three evaluated parameters was highly consistent with the result provided by the manufacturer. The TOPSIS-based method serves as a suitable evaluation tool for the overall performance of TSCs. It facilitates the optimal deployment of TSCs for industrial purposes.

scholarly journals A Modified Salp Swarm Algorithm Based on the Perturbation Weight for Global Optimization Problems

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Yuqi Fan ◽  
Junpeng Shao ◽  
Guitao Sun ◽  
Xuan Shao
Keyword(s):  
Large Scale ◽  
Optimization Problems ◽  
Iteration Process ◽  
Function Optimization ◽  
Search Performance ◽  
Global Function ◽  
Salp Swarm Algorithm ◽  
Search Speed ◽  
Swarm Algorithm ◽  
Feasible Solutions

Metaheuristic algorithms are often applied to global function optimization problems. To overcome the poor real-time performance and low precision of the basic salp swarm algorithm, this paper introduces a novel hybrid algorithm inspired by the perturbation weight mechanism. The proposed perturbation weight salp swarm algorithm has the advantages of a broad search scope and a strong balance between exploration and exploitation and retains a relatively low computational complexity when dealing with numerous large-scale problems. A new coefficient factor is introduced to the basic salp swarm algorithm, and new update strategies for the leader position and the followers are introduced in the search phase. The new leader position updating strategy has a specific bounded scope and strong search performance, thus accelerating the iteration process. The new follower updating strategy maintains the diversity of feasible solutions while reducing the computational load. This paper describes the application of the proposed algorithm to low-dimension and variable-dimension functions. This paper also presents iteration curves, box-plot charts, and search-path graphics to verify the accuracy of the proposed algorithm. The experimental results demonstrate that the perturbation weight salp swarm algorithm offers a better search speed and search balance than the basic salp swarm algorithm in different environments.

Quotient Space-Based Boundary Condition for Particle Swarm Optimization Algorithm

2011 ◽  
Vol 3 (1) ◽  
pp. 78-89
Author(s):  
Yuhong Chi ◽  
Fuchun Sun ◽  
Langfan Jiang ◽  
Chunyang Yu ◽  
Chunli Chen
Keyword(s):  
Boundary Condition ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Quotient Space ◽  
Particle Swarm ◽  
Search Space ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Search Speed

To control particles to fly inside the limited search space and deal with the problems of slow search speed and premature convergence of particle swarm optimization algorithm, this paper applies the theory of topology, and proposed a quotient space-based boundary condition named QsaBC by using the properties of quotient space and homeomorphism. In QsaBC, Search space-zoomed factor and Attractor are introduced according to the dynamic behavior and stability of particles, which not only reduce the subjective interference and enforce the capability of global search, but also enhance the power of local search and escaping from an inferior local optimum. Four CEC’2008 benchmark functions are selected to evaluate the performance of QsaBC. Comparative experiments show that QsaBC can achieve the satisfactory optimization solution with fast convergence speed. Furthermore, QsaBC is more effective with errant particles, and has easier calculation and better robustness than other methods.

scholarly journals Attention and the motion aftereffect

2019 ◽  
Vol 72 (12) ◽  
pp. 2848-2864 ◽  
Author(s):  
Michael J Morgan ◽  
Joshua A Solomon
Keyword(s):  
Time Course ◽  
Reaction Times ◽  
Motion Aftereffect ◽  
Movement Direction ◽  
Search Performance ◽  
Motion Adaptation ◽  
Test Array ◽  
Percent Correct ◽  
Search Speed ◽  
The One

We measured the effects of attentional distraction on the time course and asymptote of motion adaptation strength, using visual search performance (percent correct and reaction time). In the first two experiments, participants adapted to a spatial array of moving Gabor patches, either all vertically oriented (Experiment 1) or randomly oriented (Experiment 2). On each trial, the adapting array was followed by a test array in which all of the test patches except one were identical in orientation and movement direction to their retinotopically corresponding adaptors, but the target moved in the opposite direction to its adaptor. Participants were required to identify the location of the changed target with a mouse click. The ability to do so increased with the number of adapting trials. Neither search speed nor accuracy was affected by an attentionally demanding conjunction task at the fixation point during adaptation, suggesting low-level (preattentive) sites in the visual pathway for the adaptation. In Experiment 3, the same participants were required to identify the one element in the test array that was slowly moving. Reaction times in this case were elevated following adaptation, but once again there was no significant effect of the distracting task upon performance. In Experiment 4, participants were required to make eye movements, so that retinotopically corresponding adaptors could be distinguished from spatiotopically corresponding adaptors. Performance in Experiments 1 and 2 correlated positively with reaction times in Experiment 3, suggesting a general trait for adaptation strength.

scholarly journals A Novel Method for Economic Dispatch of Combined Heat and Power Generation

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3113 ◽  
Author(s):  
Bach Dinh ◽  
Thang Nguyen ◽  
Nguyen Quynh ◽  
Le Dai
Keyword(s):  
Power Generation ◽  
Economic Dispatch ◽  
Bat Algorithm ◽  
Combined Heat And Power ◽  
Search Performance ◽  
Optimal Range ◽  
Different Types ◽  
Search Speed ◽  
Novel Method ◽  
Modified Bat Algorithm

The paper proposes a modified Bat algorithm (MBA) for searching optimal solutions of Economic dispatch of combined heat and power generation (CHPGED) with the heat and power generation from three different types of units consisting of pure power generation units, pure heat generation units and cogeneration units. The CHPGED problem becomes complicated and big challenge to optimization tools since it considers both heat and power generation from cogeneration units. Thus, we apply MBA method with the purpose of enhancing high quality solution search ability as well as search speed of conventional Bat algorithm (BA). This proposed approach is established based on three modifications on BA. The first is the adaptive frequency adjustment, the second is the optimal range of updated velocity, and the third is the retained condition of a good solution with objective to ameliorate the search performance of traditional BA. The effectiveness of the proposed approach is evaluated by testing on 7, 24, and 48 units systems and IEEE 14-bus system and comparing results with BA together with other existing methods. As a result, it can conclude that the proposed MBA method is a favorable meta-heuristic algorithm for solving CHPGED problem

A Combined Local Best Particle Swarm Optimization Algorithm

2013 ◽  
Vol 333-335 ◽  
pp. 1388-1391
Author(s):  
Zhi Gang Lian ◽  
Ye Jun Gao ◽  
Chun Lei Ji ◽  
Xue Wu Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Local Information ◽  
Experimental Results ◽  
Local Optimum ◽  
Search Performance ◽  
Swarm Optimization

This paper proposes a combined local best particle swarm optimization algorithm (CLBPSO) which combined with local optimum particle information. And it gives three ways of combination local information. Experimental results indicate that the CLBPSO algorithm improves the search performance on the benchmark functions significantly. On the basis of experimental results, we will also compare these three methods with each other to find the best one.

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