Pheromone Concentration Research Articles

Quantum-inspired firefly algorithm with ant miner plus for fake news detection

Nowadays, technology has shifted the way individuals access news from conventional media sources to social media platforms. The active engagement of people with social media platforms leads them to consume news without confirming its source or legitimacy. This facilitated the dissemination of more manipulated and false information in the form of rumors and fake news. Fake news can affect public opinion and create chaos and panic among the population. Thus, it is essential to employ an advanced methodology to identify fake news with high precision. This research work has proposed the concept of the quantum-inspired firefly algorithm with the ant miner plus algorithm (QFAMP) for more effective fake news detection. The proposed QFAMP algorithm utilizes the attributes of quantum computing (QC), the firefly algorithm (FA), and the ant miner plus algorithm (AMP). Here, the QFA approach ensures the effective exploitation of the firefly agents until the agents are able to search for the brighter firefly. Further, the AMP algorithm utilizes the best ants with higher pheromone concentrations for global exploration, which also avoids the premature convergence of the QFA agents. In addition, the AMP algorithm serves as an efficient data mining variant that is effective for the classification of fake news. The efficacy of the proposed QFAMP algorithm is evaluated for the dataset of FakeNewsNet, which is composed of two sub-categories: BuzzFeed and PolitiFact. The experimental evaluations indicate the effective performance of the proposed algorithm compared to the other techniques.

A Review of Improved Methods for Ant Colony Optimization in Path Planning

_ Ant colony optimization is an intelligent evolutionary algorithm that has been widely used in the field of path planning, and the research on its improved methods is proliferating. In this paper, the improvement methods of single and hybrid ant colony optimization in path planning are reviewed in terms of initial pheromone concentration, heuristic function, state transition rule, pheromone update rule, and parameter optimization, respectively. The future research directions of ant colony optimization have been prospected in terms of theoretical aspects, parameter setting and optimization, integration with other algorithms, and multiobjectives. Keywords ant colony optimization; path planning; single ant colony optimization; hybrid ant colony optimization; improvement methods

Logistics distribution route optimization of electric vehicles based on distributed intelligent system

Abstract The data management system of health cloud authentication plays an important role in the optimization of logistics vehicle routing. It can not only help logistics vehicles choose the best distribution path, but also save time and cost and improve economic efficiency. At present, logistics has not yet formed a complete service system. High distribution costs and low distribution efficiency limit the development of the entire logistics. The reduction of logistics costs and the improvement of distribution efficiency have become the top priorities of the society. The optimization of the distribution route is the key to cost saving and distribution logistics. It is particularly important to study and optimize the distribution route, because the distribution route affects the logistics transportation efficiency and the loss cost during transportation. Therefore, this paper adjusted the scheduling system of logistics vehicles through a distributed intelligent system, and optimized the path of logistics vehicles according to the improved genetic algorithm, thereby reducing the transportation cost of logistics and improving the efficiency of logistics distribution. This article first explains the definition, classification, and main components of the delivery vehicle routing problem. Then, using cloud authentication path optimization, a distributed intelligent system is constructed. Finally, an improved ant colony algorithm is used to analyze and study the distance constraints of vehicles. By improving the ant colony algorithm, it can be seen that the optimized path pheromone concentration and the optimized sub-function have gradually increased with time. The mean pheromone concentration was 40 %, and the seventh day was 15 % higher than the first. The mean value of the optimized subfunction was 0.34 %, and the seventh day was 20 % higher than the first. The distribution cost and distribution efficiency of the optimized logistics vehicle distribution path were much higher than those of the traditional logistics distribution path. Moreover, the distribution cost of the logistics distribution path was 9 % lower than the traditional one, and the distribution efficiency was 13 % higher. The average smoothness of the optimized logistics path is about 90 %, and the seventh day is 11 % higher than the first day. The average fitness of the optimized logistics path is 88 %, and the seventh day is 14 % higher than the first day. In a word, the data management system can uniformly schedule logistics vehicles and improve the efficiency of distribution.

The roles of yeast formins and their regulators Bud6 and Bil2 in the pheromone response.

In response to pheromone Saccharomyces cerevisiae extend a mating projection. This process depends on the formation of polarized actin cables which direct secretion to the mating tip and translocate the nucleus for karyogamy. Here, we demonstrate that proper mating projection formation requires the formin Bni1, as well as the actin nucleation promoting activities of Bud6, but not the formin Bnr1. Further, Bni1 is required for pheromone gradient tracking. Our work also reveals unexpected new functions for Bil2 in the pheromone response. Previously we identified Bil2 as a direct inhibitor of Bnr1 during vegetative cell growth. Here, we show that Bil2 has Bnr1-independent functions in spatially focusing Bni1-GFP at mating projection tips, and in vitro Bil2 and its binding partner Bud6 organize Bni1 into clusters that nucleate actin assembly. bil2∆ cells also display entangled Bni1-generated actin cable arrays and defects in secretory vesicle transport and nuclear positioning. At low pheromone concentrations, bil2∆ cells are delayed in establishing a polarity axis, and at high concentrations they prematurely form a second and a third mating projection. Together, these results suggest that Bil2 promotes the proper formation and timing of mating projections by organizing Bni1 and maintaining a persistent axis of polarized growth.

Whether search directions number affects the efficiency of the path planning algorithm: Taking an improved ACO algorithm with 32 directions for example

An improved Ant Colony Optimization (ACO) algorithm, named IACO, is proposed to address the inherent limitation of slow convergence, susceptibility to local optima and excessive number of inflection in traditional ACO when solving path planning problems. To this end, firstly, the search direction number is expanded from 4 or 8 into 32; Secondly, the distance heuristic information is replaced by an area heuristic function, which deviated from the traditional approach that only considers pheromone information between two points; Then, the influence of path angle and number of turns is taken into account in the local pheromone update. Additionally, a reward and punishment mechanism is employed in the global pheromone update to adjust the pheromone concentrations of different paths; Furthermore, an adaptive update strategy for pheromone volatility factor adaptive is proposed to expand the search range of the algorithm. Finally, simulation experiments are conducted under various scenarios to verify the superiority and effectiveness of the proposed algorithm.

Dynamic evacuation path planning for subway station fire based on IACO

Fire in subway stations is the most serious type of accident causing casualties, and traditional static evacuation paths are no longer able to guarantee evacuation safety. To solve this problem, this study proposes an improved ant colony algorithm (IACO), which dynamically adjusts the evacuation paths by combining the simulation results of simulation software (PyroSim), and extends the available safe evacuation time of the personnel in a limited way. First, PyroSim is used to simulate the subway station hall and platform fire scenarios with the temperature, carbon monoxide (CO) concentration, and visibility at each location, obtained from twelve sets of monitors. Improvements in the pheromone concentration and update strategy of the ant colony algorithm (ACO) can improve the accuracy and convergence speed of the algorithm. Optimal path planning is achieved by the IACO, and then evacuation paths are dynamically adjusted by comparing the time at which each monitoring point on the path reaches the human hazard thresholds. Finally, until no adjustments can be made to obtain an evacuation path that extends the available safe evacuation time for personnel in a limited way. Compared to the traditional static evacuation path, this study provides a dynamic evacuation path that takes into account the real-time changes of fires in subway stations. This dynamic evacuation path planning approach will be put into use in subway stations before the accident to provide guidance on fire safety in subway stations and to provide contingency plans for emergency evacuation.

Research on path planning of tea picking robot based on ant colony algorithm

Robot tea picking is an inevitable trend to solve the problem of tea picking, and the picking path planning is directly related to the robot picking efficiency. An Improved Ant Colony Algorithm (IACA) is proposed, which firstly introduces the adaptive adjustment mechanism into the pheromone volatilization factor of the ant colony algorithm, and then sets the pheromone volatilization factor with a high initial value to improve the searching speed, and then adjusts the size of its value within a certain range in real time according to the iterative results, and finally solves the problem that the searching of the ant colony algorithm is prone to fall into the local optimal solution. On the basis of visual recognition of tea leaves and obtaining coordinate information, the improved ant colony algorithm is used to enter the path planning simulation experiments, and the planning results of the other six algorithms are compared with the similar algorithms and dissimilar algorithms, and the experimental results indicate that the IACA method has improved the shortest path index by 5% compared to the basic ant colony algorithm, and by an average of 4% compared to similar improved ant colony algorithms. In comparison to different optimization algorithms, the enhancement has an average increase of 6%; Furthermore, the convergence speed has been improved by 60% compared to six other methods. The standard deviation of repeated experimental results is 50% lower than the other six methods. The gap between the results of multiple repeated experiments is small, the degree of fluctuation is low, and the calculation results are more stable, which verifies the superiority of IACA method. Therefore, the improvement of the ant colony algorithm makes the pheromone concentration value with adaptive adjustment ability, which reflects good effects in path optimization, convergence speed improvement, stability of results, etc., and has good application value for the path planning problems such as tea picking, which has complex paths and large computational volume.

Islands of cooperation emerge by stigmergic interactions in iterated spatial games.

This article focuses on the emergence of cooperation in societies of self-interested agents. In particular, it introduces a mechanism based on indirect-stigmergic-interactions between agents moving in an environment, to express the likeliness of finding cooperative partners. On the one hand, agents that find themselves cooperating with others emit pheromones in their current location, expressing the presence of agents willing to cooperate. On the other hand, agents that sense pheromones tend to move towards regions with a higher pheromone concentration. Results show that this mechanism leads to the emergence of spatial regions where cooperation can be effectively sustained, and in which agents can overall get better payoffs than those agents not taking into account pheromones in their choices.

基于蚁群-改进人工势场法的农业搬运机器人路径规划分析

Aiming at the problems of low efficiency and slow function convergence of the ant colony algorithm in path planning of agricultural transport robots, a fusion algorithm combined with the artificial potential field method was proposed. Firstly, the function of each parameter was analyzed according to the mathematical model of the traditional ant colony algorithm, followed by the simulation analysis of the optimal parameters through grid map modeling in MATLAB and data recording. Secondly, the deficiency of the classical artificial potential field method in agriculture, i.e., it could not arrive at the endpoint or realize local locking, was improved by introducing the intermediate point and the relative distance of the target. Finally, the features of the two algorithms were combined and the improved artificial potential field method was integrated with the traditional ant colony algorithm so that the improved artificial potential field method could play a dominant role in the initial path planning stage of agricultural transport robots while the ant colony algorithm exerted the main effect in the later stage with the increase in the pheromone concentration. It was verified through simulation analysis, it was verified that the fusion algorithm of ant colony algorithm and improved artificial potential field method outperforms traditional ant colony algorithm in terms of farthest path, optimal path, running time, and iteration number.

Wireless sensor network routing optimization based on improved ant colony algorithm in the Internet of Things

The Internet of Things (IoT) connects devices, enabling real-time data acquisition, automation, and collaboration. Wireless sensor networks are one of the important components of the Internet of Things, consisting of many wireless sensor nodes distributed in space. These nodes can perceive environmental information and transmit it to other nodes through wireless communication. In wireless sensor network routing optimization methods, improved ant colony algorithm can be used to find the optimal routing scheme. Ant colony algorithm simulates the behavior of ants in the process of searching for food, and optimizes factors such as transfer probability and pheromone concentration to enable ants to find the shortest path. In wireless sensor networks, node positions can be used as reference nodes and anchor nodes, combined with the objective function of wireless sensor network routing optimization, and improved ant colony algorithm can be used to solve the optimal path, thus obtaining the optimal wireless sensor network routing optimization scheme. Through experimental results, it can be found that the proposed method performs well in terms of energy consumption, transmission delay, number of dead nodes, and network throughput. These optimization results have positive implications for the sustainable development and practical application of the Internet of Things, which can improve the development of the digital economy and enhance the construction of smart cities.

Research on global path planning of intelligent vehicles based on improved ant colony algorithm

According to the needs of intelligent vehicles, this paper proposes a global path planning algorithm based on improved ant colony algorithm to address the problems of slow convergence speed and multiple path bends in ant colony algorithm. By redefining the heuristic function of ant colony algorithm, adopting fallback and fill mechanisms, setting safe distances, modifying pheromone concentration update rules, and limiting the range of pheromone concentration, the ant colony algorithm is improved. And by deleting redundant points on the path, selecting key turning points and cubic B-spline curves for path smoothing, the planned path is shortest and smooth, which can meet the needs of path following. Through simulation, the improved ant colony algorithm has the advantages of fast convergence and smooth path.

Ant colony optimization-based adjusted PID parameters: a proposed method

The ant colony algorithm (ACA) is a heuristic algorithm that resolves the optimality problem by simulating an ant’s foraging process, which finds the shortest path. The connotation of the ACA is to find the optimal solution. The Proportional Integral Derivative (PID) parameter tuning is an essential tool in the control field and includes three parameters, Kp, Ki, and Kd, to achieve the best control effect. Besides, tuning the PID parameters is closely related to finding the “optimal” solution that can be attained based on the feasible combination of the two. This article transforms the PID parameter tuning problem into an ACA that finds the optimal solution called ACA-based PID parameters tuning. Furthermore, PID control is simulated by setting the parameters of ACA, such as ant colony size, iteration times, nodes, paths, path evaluation criteria, pheromone concentration, heuristic function, weight factor, and decision function. Eventually, the two PID controller parameter tuning strategies are compared and analyzed, and the advantages and disadvantages of each are obtained. Compared with the 4:1 attenuation curve method, the proposed method can significantly reduce the MP score of the overshoot of the system, increase the time, and improve the dynamic and steady-state performance of the system, but reduce the steady-state error of the system. Therefore, the feasibility and effectiveness of the proposed method is verified.

An Enhanced Ant Colony Algorithm-Based Low-Carbon Distribution Control Method for Logistics Leveraging Internet of Things (IoT)

This paper presents a low-carbon vehicle routing optimization model to reduce energy consumption and carbon emissions in logistics and distribution. The model is solved using a hybrid algorithm of simulated annealing and ant colony optimization. It enhances the information pheromone concentration update process and directionality by introducing a carbon emission factor and a multifactor operator. Additionally, an adaptive elite individual reproduction strategy is employed to improve algorithm efficiency. In this case study focusing on cold chain logistics distribution, both the model and algorithm under consideration were evaluated. The findings affirm the effectiveness of the model in reducing carbon emissions and demonstrate the efficiency and robustness of the algorithm. Through this analysis, the paper sheds light on environmentally sustainable practices in logistics distribution.

A Multiple Agile Satellite Staring Observation Mission Planning Method for Dense Regions

To fully harness the burgeoning array of in-orbit satellite resources and augment the efficacy of dynamic surveillance of densely clustered terrestrial targets, this paper delineates the following methodologies. Initially, we leverage the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm to aggregate the concentrated terrestrial targets, taking into account the field-of-view peculiarities of agile staring satellites. Subsequently, we architect a model for a synergistic multiple angle earth observation satellites (AEOSs) mission planning with the optimization objectives of observational revenue, minimal energy expenditure, and load balancing, factoring in constraints such as target visibility time window, AEOSs maneuverability, and satellite storage. To tackle this predicament, we propose an improved heuristic ant colony optimization (ACO) algorithm, utilizing the task interval, task priority, and the length of time a task can start observation as heuristic information. Furthermore, we incorporate the notion of the max–min ant system to regulate the magnitude of pheromone concentration, and we amalgamate global and local pheromone update strategies to expedite the convergence rate of the algorithm. We also introduce the Lévy flight improved pheromone evaporation coefficient to bolster the algorithm’s capacity to evade local optima. Ultimately, through a series of simulation experiments, we substantiate the significant performance improvements achieved by the improved heuristic ant colony algorithm compared to the standard ant colony algorithm. We furnish proof of its efficacy in resolving the planning of multiple AEOS staring observation missions.

Enhanced ant colony algorithm with obstacle avoidance strategy for multi-objective path planning of mobile robots

Path planning with multiple evaluation metrics makes the motion of a robot realistic, but the contradiction between the metrics and the lack of global search and obstacle avoidance capabilities increases the difficulty of obtaining the optimization solution. To solve these problems, an enhanced ant colony algorithm (EACA) with an obstacle avoidance strategy is proposed in this article. First, the path planning model is constructed, and strict movement rules are designed. Secondly, the EACA with global search, balancing the contradiction between metrics, is designed. The dynamic regulation of pheromone concentration and the mechanism of fluctuating pheromone distribution are explored, heuristic information is optimized and the path planning effect is enhanced. Finally, a new mechanism of away-from-obstacles is proposed as the obstacle avoidance strategy, which ensures a reasonable safe distance. Comparative simulations on several different maps validate the performance of EACA with the obstacle avoidance strategy for planning robot movement paths.

Multi-ant colony algorithm based on cooperative game and dynamic path tracking

For the problems of slow convergence and low stability in traditional ant colony algorithm when solving large-scale Traveling Salesman Problem (TSP), a multi-ant colony algorithm based on cooperative game and dynamic path tracking (CDMACA) is proposed. Firstly, a novel heterogeneous multi-population is formed by Ant Colony System (ACS) and Max–Min Ant System (MMAS). Secondly, a cooperative game is introduced. In the isomorphic populations, all the populations cooperate and then the pheromone income will be distributed to the participants based on the contribution. Populations with higher contribution will obtain more income, which can improve the accuracy of the solutions; On the other hand, in the heterogeneous populations, a learning mechanism is introduced to enable heterogeneous populations to learn the optimal solution from each other to further improve the accuracy. Thirdly, a dynamic path tracking mechanism is proposed to reward or punish the public paths with pheromone based on the similarity of the optimal solutions, which can enhance the concentration of pheromone on the optimal path, thus improving the convergence. Finally, when the algorithm stalls, a pheromone balance mechanism is introduced. By dynamically cutting the pheromone on the current optimal path, the probability of the optimal path being chosen will decrease, which can help the algorithm jump out of the local optimum. Through a large number of TSP instances, the proposed algorithm in this paper can further improve the accuracy when solving large-scale TSP compared with other improved algorithms.

Research on Terminal Distance Index-Based Multi-Step Ant Colony Optimization for Mobile Robot Path Planning

Aiming at the path planning problem of mobile robot, when the traditional ant colony optimization (ACO) is simulated in grid model, it is assumed that ants can only move to adjacent nodes, that is, the step size is 1 and there are 8 movable directions. However, in practice, the moving direction of the ant is completely free. Therefore, an improved terminal distance index-based multi-step ant colony optimization (TDI-MSACO) for mobile robot path planning is proposed in this paper. A multi-step ant colony optimization (MSACO) is firstly used to improve the flexibility of ant’s movement and the path obtained by using MSACO is shorter and more in line with the actual situation, through the simulation of a large number of cases, it is determined that the optimal step size is 2 or 3. In addition, aiming at the problems of pheromone updating mechanism in MSACO, a concept of terminal distance index (TDI) is proposed to replace pheromone concentration and accelerate the convergence speed of the MSACO. In order to verify the effectiveness of the improved TDI-MSACO, the simulations are tested on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$20\times 20$</tex-math> </inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$30\times 30$</tex-math> </inline-formula> , and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$50\times 50$</tex-math> </inline-formula> grid models and the results show that the improved TDI-MSACO has faster convergence speed and shorter path. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —the motivation of this paper comes from the need to develop a fast and efficient path planning method for practical applications such as material transport in shop floor, cleaning, monitoring of dangerous radioactive sites and military applications, and so on. The ant colony optimization (ACO), inspired from the foraging behavior of ant species, is a swarm intelligence algorithm for solving hard combinatorial optimization problems. The ACO is widely used in robot path planning areas because of its characteristics of positive feedback, distributed computation. However, it has the weaknesses of premature convergence and low search speed, which greatly hinder its application. To improve the performance of the ACO, an improved terminal distance index-based multi-step ant colony optimization (TDI-MSACO) for mobile robot path planning is proposed in this paper. Multi-step and terminal distance index are used to improve the flexibility of ant movement and accelerate the convergence speed, respectively. The effectiveness of the TDI-MSACO method for solving mobile path planning problems is proved by comparing the simulation results with those previously presented in the literature. Furthermore, the proposed method can be expanded to the dynamic environment containing multiple static and dynamic obstacles of many sizes and forms, it also can be used to solve the energy-efficient path planning problems. Meanwhile, it can be further modified and applied to the simultaneous scheduling of machines and transport robots in an actual flexible job shop environment.

Location and path planning for urban emergency rescue by a hybrid clustering and ant colony algorithm approach

Rescue station setup and rescue path planning are two important tasks in urban emergency rescue. The former task ensures rescue response capability and the latter task provides effective rescue solutions. When emergencies occur in cities, evacuees are distributed along the urban road network. Rescue resources refer to rescue vehicles whose available number and capacity are both limited. With the constraints of rescue resources and the number of rescues, this paper aims to simultaneously optimize the tasks of rescue station setup and rescue path planning. In the addressed scenario, the priority of each evacuee is quantified as a weight value that is used as the main optimization objective. To solve the problem, a comprehensive urban emergency rescue planning approach is proposed. The proposed approach consists of components of road network processing, road network weight calculation, rescue station setup and rescue path planning. For the setup of rescue stations, this paper employs a clustering method to provide a set of high-quality candidate rescue stations for subsequent path planning based on the locations of evacuees and the road network structure. For rescue path planning, an improved ant colony optimization algorithm is developed. The proposed method is called the planning algorithm with clustering and improved ant colony optimization (PA-C-IACO). The proposed PA-C-IACO redefines the degree of heuristic and pheromone concentration increments for transfer between intersections in the ant colony algorithm and incorporates a reward mechanism during the pheromone update process. Experimental results on six different size datasets show that PA-C-IACO outperforms state-of-the-art algorithms and shows good robustness and feasibility.

The Quadruped Robot Uses the Trajectory Planned by DIACO to Complete the Obstacle Avoidance Task

The diffusion-improved ant colony optimization (DIACO) algorithm, as introduced in this paper, addresses the slow convergence speed and poor stability of the ant colony optimization (ACO) in obstacle avoidance path planning for quadruped robots. DIACO employs a nonuniformly distributed initial pheromone, which reduces the blind search time in the early stage. The algorithm updates the heuristic information in the transition probability, which allows ants to better utilize the information from the previous iteration during their path search. Simultaneously, DIACO adjusts the pheromone concentration left by ants on the path based on the map information and diffuses the pheromone within a specific range following the artificial potential field algorithm. In the global pheromone update, DIACO adjusts the pheromone on both the optimal path and the worst path generated by the current iteration, thereby enhancing the probability of ants finding the optimal path in the subsequent iteration. This paper designs a steering gait based on the tort gait to fulfill the obstacle avoidance task of a quadruped robot. The effectiveness of the DIACO algorithm and steering gait is validated through a simulation environment with obstacles constructed in Adams. The simulation results reveal that DIACO demonstrates improved convergence speed and stability compared to ACO, and the quadruped robot effectively completes the obstacle avoidance task using the path planning provided by DIACO in combination with the steering gait.

Monte Carlo-based improved ant colony optimization for path planning of welding robot

For spot-welding process, rational path planning of weld points can improve productivity for welding robot. In basic ant colony optimization, at the beginning of the iteration, the pheromone concentration is only related to the path length, but at this time, the path often contains a lot of redundant parts. Therefore, the pheromone in the initial iteration is almost difficult to reflect the advantages and disadvantages of the node, and it requires multiple iterations to slowly show the advantages and disadvantages of the path. For this shortcoming, a Monte Carlo-based improve ant colony optimization (MC-IACO) is proposed to solve the path planning of welding robots. The MC-IACO algorithm samples the path results generated in each generation and converts them into Monte Carlo indexes of the nodes. By substituting the Monte Carlo index of the feasible node into the sigmoid function, the probability that the corresponding node may constitute the optimal path, namely the Monte Carlo factor, is calculated to help ants make decisions. The new node transfer formula constructed with this factor can effectively improve the path finding efficiency of ants and the efficiency of the algorithm to complete the path planning task. The feedback adjustment factor is introduced to make the judgment of the advantages and disadvantages of feasible nodes also refer to the advantages and disadvantages of subsequent nodes. The simulation results show that compared with the basic ant colony optimization and other improved ant colony optimization, the MC-IACO algorithm in this paper improves significantly in solving the path planning problem of the welding robot, and its overall performance is better.