Searching Ability of PSO with Non-Convergent Particles

2014 ◽  
Vol 1 ◽  
pp. 150-153
Author(s):  
Takeshi Kamio ◽  
Yuhei Itaki ◽  
Hisato Fujisaka ◽  
Kazuhisa Haeiwa
Keyword(s):  
Searching Ability

Does food-searching ability determine habitat selection? Foraging in sand of three species of gerbilline rodents

Ecography ◽  
2000 ◽  
Vol 23 (1) ◽  
pp. 122-129 ◽  
Author(s):  
Boris R. Krasnov ◽  
Georgy I. Shenbrot ◽  
Luis E. Rios ◽  
Maria E. Lizurume
Keyword(s):  
Habitat Selection ◽  
Searching Ability

Growth, Development and Innate Capacity for Increase in Aphytis Chrysomphali Mercet and A. Melinus Debach, Parasites of California Red Scale, Aonidiella Aurantii (Mask.), In Relation to Temperature.

1974 ◽  
Vol 22 (2) ◽  
pp. 213 ◽  
Author(s):  
I Abdelrahman
Keyword(s):  
Negative Relationship ◽  
Differential Mortality ◽  
Female Progeny ◽  
Annual Fluctuation ◽  
Adaptation To Cold ◽  
Rate Of Increase ◽  
Total Progeny ◽  
Searching Ability ◽  
Pupal Mortality ◽  
Threshold Of Development

A, melinus produced more female progeny and more than twice as many total progeny as A. chrysomphali; it also destroyed almost twice as many hosts through oviposition and mutiliation. A. chrysomphali had a longer post-oviposition period than A. melinus, especially at 30�C. The proportion of single progeny in a host was higher for A, chrysomphali than for A. melinus at all temperatures, and was related to temperature positively in A. chrysomphali and inversely in A. melinus. Large old female A. melinus produced only males at the end of their lives; they did not mate at that stage when offered males, not because they were aged but because they mate only once in their lives. As temperature decreased, female A. melznus ceased producing females earlier, probably because temperature affected either longevity of sperms or the mechanism controlling their release. Differential mortality, temperature, and age of mothers all influenced sex ratio. Pupal mortality was inversely related to temperature within the observed range 20-30�C; in female pupae of A. chrysomphali it was lower than that in either female or male pupae of A. melinus; it was higher in male than female pupae in A. melinus. A. melinus lived longer than A. chrysomphali at all temperatures. Duration of development was longer for A. chrysomphali than for A. melinus at 30�C, but shorter at 20 and 25�C. The threshold of development was 8.5C for A. chrysomphali and 11C for A. melinus. A. chrysomphali had a higher rm at 20 and 25�C than A. melinus, but much lower at 30�C. The highest rate of increase was at > 30�C for A. melinus, and at about 25�C for A. chrysomphali. The rm of the parasites was 3.1-5.0 times that of red scale, depending on parasite species and temperature. A. chrysomphali is smaller than A. melinus, and from the positive relationship between adaptation to cold and speed of development, and the negative relationship between speed of development and size, a negative relationship between size and adaptation to cold within Aphytis spp. may be postulated. A. chrysomphali is more adapted to cold and less to heat than A. melinus. This explains the seasonal and annual fluctuation in their relative abundance in southern Australia. The species would complement each other in controlling red scale; from the data presented here it is possible that Aphytis spp. in Australia may have evolved into more efficient control agents of red scale than elsewhere. Knowledge on the searching ability of Aphytis at different host densities is wanting.

scholarly journals Allocation and Scheduling of Handling Resources in the Railway Container Terminal Based on Crossing Crane Area

2021 ◽  
Vol 13 (3) ◽  
pp. 1190
Author(s):  
Gang Ren ◽  
Xiaohan Wang ◽  
Jiaxin Cai ◽  
Shujuan Guo
Keyword(s):  
Programming Model ◽  
Container Terminal ◽  
Simultaneous Optimization ◽  
Mixed Integer ◽  
Gantry Crane ◽  
Optimization Scheme ◽  
Long Distance ◽  
Proposed Model ◽  
Hybrid Heuristic Algorithm ◽  
Searching Ability

The integrated allocation and scheduling of handling resources are crucial problems in the railway container terminal (RCT). We investigate the integrated optimization problem for handling resources of the crane area, dual-gantry crane (GC), and internal trucks (ITs). A creative handling scheme is proposed to reduce the long-distance, full-loaded movement of GCs by making use of the advantages of ITs. Based on this scheme, we propose a flexible crossing crane area to balance the workload of dual-GC. Decomposing the integrated problem into four sub-problems, a multi-objective mixed-integer programming model (MIP) is developed. By analyzing the characteristic of the integrated problem, a three-layer hybrid heuristic algorithm (TLHHA) incorporating heuristic rule (HR), elite co-evolution genetic algorithm (ECEGA), greedy rule (GR), and simulated annealing (SA) is designed for solving the problem. Numerical experiments were conducted to verify the effectiveness of the proposed model and algorithm. The results show that the proposed algorithm has excellent searching ability, and the simultaneous optimization scheme could ensure the requirements for efficiency, effectiveness, and energy-saving, as well as the balance rate of dual-GC.

Displacement and dispersion of parturient caribou at calving as antipredator tactics

1987 ◽  
Vol 65 (7) ◽  
pp. 1597-1606 ◽  
Author(s):  
A. T. Bergerud ◽  
R. E. Page
Keyword(s):  
Snow Cover ◽  
Rangifer Tarandus ◽  
Forest Cover ◽  
Ursus Arctos ◽  
Early Years ◽  
Stochastic Variation ◽  
Spring Phenology ◽  
Calf Survival ◽  
Searching Ability ◽  
High Elevations

Survival of caribou (Rangifer tarandus) calves until 4 months of age was monitored for 8 years in four herds in northern British Columbia, Canada. The chief cause of mortality was predation by wolves (Canis lupus) and grizzly bears (Ursus arctos) and this mortality was correlated within years between all herds. More calves died in years with late springs when extensive snow patches remained during calving in June than in early springs when larger snow-free areas existed. Before calving and after birth, caribou cows sought to space themselves out on snow-free areas in small aggregations at high elevations above treeline. By placing themselves at high elevations, the females increased the distance between themselves and wolves and bears travelling in the valley bottoms, as well as the main alternate prey, moose (Alces alces), which calved only in forest cover at lower elevations. In addition, the reduced snow in early springs meant that there was more space for dispersion. The variation in calf survival for three herds was negatively correlated with the heterogeneity of the calving area. Snow cover disappeared in smaller patches in more rugged mountains regardless of spring phenology, thereby providing a more constant search area for predators from year to year. More uniform mountains had either extensive areas of snow cover (late years) or brown substrates (early years), thus greatly varying the space that predators had to search between years. As stochastic variation in snow cover at calving time alters the searching ability of predators, the aggregation responses of prey, and the spatial overlap between predators and prey, it promotes short-term stability of the prey and lessens the probability of extinction.

Research and Improvement on K-Means Clustering Algorithm

2013 ◽  
Vol 756-759 ◽  
pp. 3231-3235
Author(s):  
Xue Mei Wang ◽  
Jin Bo Wang
Keyword(s):  
Differential Evolution ◽  
Clustering Algorithm ◽  
Differential Evolution Algorithm ◽  
Optimal Solution ◽  
Clustering Quality ◽  
Local Optimal Solution ◽  
Evolution Algorithm ◽  
Clustering Center ◽  
Searching Ability ◽  
Improved Differential Evolution Algorithm

According to the defects of classical k-means clustering algorithm such as sensitive to the initial clustering center selection, the poor global search ability, falling into the local optimal solution. A differential evolution algorithm which was a kind of a heuristic global optimization algorithm based on population was introduced in this article, then put forward an improved differential evolution algorithm combined with k-means clustering algorithm at the same time. The experiments showed that the method has solved initial centers optimization problem of k-means clustering algorithm well, had a better searching ability,and more effectively improved clustering quality and convergence speed.

scholarly journals A New Fruit Fly Optimization Algorithm Based on Differential Evolution

2015 ◽  
Vol 3 (4) ◽  
pp. 365-373 ◽  
Author(s):  
Dabin Zhang ◽  
Jia Ye ◽  
Zhigang Zhou ◽  
Yuqi Luan
Keyword(s):  
Differential Evolution ◽  
Optimization Algorithm ◽  
Fruit Fly ◽  
Local Extremum ◽  
Experimental Results ◽  
Fruit Fly Optimization Algorithm ◽  
A Algorithm ◽  
Fruit Fly Optimization ◽  
Searching Ability ◽  
Selection Of

Abstract In order to overcome the problem of low convergence precision and easily relapsing into local extremum in fruit fly optimization algorithm (FOA), this paper adds the idea of differential evolution to fruit fly optimization algorithm so as to optimizing and a algorithm of fruit fly optimization based on differential evolution is proposed (FOADE). Adding the operating of mutation, crossover and selection of differential evolution to FOA after each iteration, which can jump out local extremum and continue to optimize. Compared to FOA, the experimental results show that FOADE has the advantages of better global searching ability, faster convergence and more precise convergence.

Study on Multi-Depot Vehicle Routing Problem Based on Cloud Adaptive Particle Swarm Optimization

2012 ◽  
Vol 253-255 ◽  
pp. 1369-1373
Author(s):  
Tie Jun Wang ◽  
Kai Jun Wu
Keyword(s):  
Particle Swarm Optimization ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Cloud Model ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Routing Problem ◽  
Adaptive Particle Swarm Optimization ◽  
Searching Ability

Multi-depots vehicle routing problem (MDVRP) is a kind of NP combination problem which possesses important practical value. In order to overcome PSO’s premature convergence and slow astringe, a Cloud Adaptive Particle Swarm Optimization(CAPSO) is put forward, it uses the randomicity and stable tendentiousness characteristics of cloud model, adopts different inertia weight generating methods in different groups, the searching ability of the algorithm in local and overall situation is balanced effectively. In this paper, the algorithm is used to solve MDVRP, a kind of new particles coding method is constructed and the solution algorithm is developed. The simulation results of example indicate that the algorithm has more search speed and stronger optimization ability than GA and the PSO algorithm.

scholarly journals A Dynamic Adjusting Novel Global Harmony Search for Continuous Optimization Problems

Symmetry ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 337 ◽  
Author(s):  
Chui-Yu Chiu ◽  
Po-Chou Shih ◽  
Xuechao Li
Keyword(s):  
Optimization Problems ◽  
Harmony Search ◽  
Continuous Optimization ◽  
Genetic Mutation ◽  
Dynamic Adjustment ◽  
Mutation Probability ◽  
Adjustment Strategy ◽  
Improved Harmony Search ◽  
Searching Ability ◽  
Continuous Optimization Problems

A novel global harmony search (NGHS) algorithm, as proposed in 2010, is an improved algorithm that combines the harmony search (HS), particle swarm optimization (PSO), and a genetic algorithm (GA). Moreover, the fixed parameter of mutation probability was used in the NGHS algorithm. However, appropriate parameters can enhance the searching ability of a metaheuristic algorithm, and their importance has been described in many studies. Inspired by the adjustment strategy of the improved harmony search (IHS) algorithm, a dynamic adjusting novel global harmony search (DANGHS) algorithm, which combines NGHS and dynamic adjustment strategies for genetic mutation probability, is introduced in this paper. Moreover, extensive computational experiments and comparisons are carried out for 14 benchmark continuous optimization problems. The results show that the proposed DANGHS algorithm has better performance in comparison with other HS algorithms in most problems. In addition, the proposed algorithm is more efficient than previous methods. Finally, different strategies are suitable for different situations. Among these strategies, the most interesting and exciting strategy is the periodic dynamic adjustment strategy. For a specific problem, the periodic dynamic adjustment strategy could have better performance in comparison with other decreasing or increasing strategies. These results inspire us to further investigate this kind of periodic dynamic adjustment strategy in future experiments.

Analysis of the Dynamic Characteristics of the Firefly Algorithm

2020 ◽  
pp. 100-115
Author(s):  
Takuya Shindo
Keyword(s):  
Heuristic Algorithm ◽  
Dynamic Characteristics ◽  
Firefly Algorithm ◽  
Chaotic Behavior ◽  
Fundamental Principle ◽  
Deterministic System ◽  
Rigorous Analysis ◽  
The Individual ◽  
Searching Ability ◽  
The Relationship

The firefly algorithm is a meta-heuristic algorithm, the fundamental principle of which mimics the characteristics associated with the blinking of natural fireflies. This chapter presents a rigorous analysis of the dynamics of the firefly algorithm, which the authors performed by applying a deterministic system that removes the stochastic factors from the state update equation. Depending on its parameters, the individual deterministic firefly algorithm exhibits chaotic behavior. This prompted us to investigate the relationship between the behavior of the algorithm and its parameters as well as the extent to which the chaotic behavior influences the searching ability of the algorithm.

scholarly journals Integrated Multi-Project Scheduling and Hierarchical Workforce Allocation in the ETO Assembly Process

2019 ◽  
Vol 9 (5) ◽  
pp. 885 ◽  
Author(s):  
Chun Jiang ◽  
Xiaofeng Hu ◽  
Juntong Xi
Keyword(s):  
Tabu Search ◽  
Project Scheduling ◽  
Hybrid Algorithm ◽  
Manufacturing Industry ◽  
Assembly Process ◽  
Solution Quality ◽  
Workforce Allocation ◽  
Benchmark Instances ◽  
Engineer To Order ◽  
Searching Ability

The engineer-to-order (ETO) production strategy plays an important role in today’s manufacturing industry. This paper studies integrated multi-project scheduling and hierarchical workforce allocation in the assembly process of ETO products. The multi-project scheduling problem involves the scheduling of tasks of different projects under many constraints, and the workforce allocation problem involves assigning hierarchical workers to each task. These two problems are interrelated. The task duration depends on the number of hierarchical workers assigned to the task. We developed a mathematical model to represent the problem. In order to solve this issue with the minimization of the makespan as the objective, we propose a hybrid algorithm combining particle swarm optimization (PSO) and Tabu search (TS). The improved PSO is designed as the global search process and the Tabu search is introduced to improve the local searching ability. The proposed algorithm is tested on different scales of benchmark instances and a case that uses industrial data from a collaborating steam turbine company. The results show that the solution quality of the hybrid algorithm outperforms the other three algorithms proposed in the literature and the experienced project manager.

Sign in / Sign up

Export Citation Format

Share Document