[Path Planning] Based on MATLAB Artificial Bee French Rules Path Planning [including MATLAB Source 004]

紫极Q1564658423

1. Introduction [/ b] [align = left] [color = RGB (0, 0, 0)] [font =&quot] 1 bee honey
The bees in nature can always find high quality honey source with extremely high efficiency in any environment, and can adapt to changes in the environment. The honeycomb system of the bee group consists of honey source, hiring bee, non-employment bee three parts, one of the advantages and disadvantages of a honey source, such as the size of the honey source, far from the honeycomb, the extraction is difficult, etc .; Employment Bee and specific honey source contacts and tells the companion in a certain probability; the responsibility of the non-employment is to find the honey source to be exploited, divided into two classes and investigating bees, follow the peak waiting for the hive waiting and detection bee is detection New honey source around the hive. When the bee picks up honey, some bees in the hive are used as a detective bee, and they are constantly looking for honey sources near the hive. If the gynesses exceeds a honey source, this investigation bee will become honey, pick After the completion of the honey, fly back to the hive dance and dance tell the follow-up. Swing dance is a basic form of communication information between bees. It conveys important information about honey from honeycomb, such as honey source and nesting distance, followed by peaks to accurately evaluate the honey source around the honeycomb. When the hung bee jumps and dances, it will return to the original honey source with some followers, follow the amount of bee depends on the quality of honey. In this way, the honey group can quickly and effectively find the highest gyon source.
[Img] https://img-blog.csdnimg.cn/20201230224504101.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1RJUUNtYXRsYWI=,size_16,color_FFFFFF,t_70 [/ img]
The group of honey honey is achieved by AC conversion and collaboration between different roles. The specific tandem process is shown in the figure. In the initial stage, the bees appeared in the form of investigum, and there is no understanding of the honey sources of the honeycomb, because there are two options for the inspection bee in the movement and external conditions: 1 becomes the hiring bee, starting around the hive Random search honey source, as shown in the figure, becomes the peak, start searching the honey source after observing the swing dance, as shown in the figure. It is assumed that two honey sources are found, after discovering honey source, the investigation bee turns into a homework, hiring the bee, remembering the location of the honey source, and puts it into the honey. After collecting honey, the bee returned to the honeycomb with a full nectar, unloading the nectar to the unloaded honey room, uninstalling the completion of the hidden honey source, giving up the honey source that the gynesence of the gyne, becoming a non-constrained Employment bees, the route in the figure; 2 jump in the recruitment area, recruit some of the peaks to be followed in the hive, lead it to return to the honey source found in the figure, the route in the figure; 3 do not recruit other bees, continue to return to the original The honey source tone is like a route in the figure. In real life, it is not all the bees to pick honey at the beginning, and most of the bees will choose to return to the recruitment area after the completion of honey, and they will recruit more bees to pick honey. [/ font] [/ color] [/ align] [align = left] [color = RGB (0, 0, 0)] [font =&quot] 2 algorithm model
The artificial house algorithm is a new intelligent optimization algorithm proposed by the simulated bee's honeycomb process, which is also made up of food source, employment bee and non-employment bee.
Food Source: Food Source is a honey source. In any of the optimization problems, the feasible solution of the problem is given in a certain form. In the artificial bee colony algorithm, the food source is the feasible solution of the optimization problem, which is the basic object to be processed in the artificial hivegor. The advantages and disadvantages of food sources are good and bad is to evaluate the size of honey source splenes.
Employment: The hiring bee is to lead the bee corresponding to the position of the food source, and a food source corresponds to a leading bee. In the artificial bee colony algorithm, the number of food sources is equal to the number of leaders. The task of leading the bee is to discover food source information and share with a certain probability and follow-up; probability is calculated as the selection of artificial bee group algorithms Strategy is generally calculated based on the method of roulette according to the adaptivity value.
Non-employment bee: Non-employment bee, including the recruitment of bee and detection bee, and the honey source information provided by the bee in the hive, and the investigum is looking for new food sources near the hive. In the artificial bee colony algorithm, follow the bee based on the information that leads the bee, search for a new food source near the food source and is greedy. If a food source is still not updated after passing, this lead the bee turns into the detection bee, and the investigum is looking for new food sources for the original food source.
[img] https://img-blog.csdnimg.cn/2020123024526317.png [/ img] [/ font] [/ color] [/ align] [align = left] [color = RGB (0, 0, 0) ] [font = 0uO0quot] 3 algorithm search process
[Img] https://img-blog.csdnimg.cn/20201230224540711.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1RJUUNtYXRsYWI=,size_16,color_FFFFFF,t_70 [/ img]
4 classification
The artificial bee colony is divided into three categories leading the bee, followed by the bee and investigating bee. Every time the search process, lead the beas and follow the bees are the food source, which is looking for the best solution, and the investigated bee is observing Whether to fall into a topically optimal, if you fall into a local best, you are randomly search for other possible food sources. Each food source represents a problem with a possible solution, and the gyrogen of the food source corresponds to the quality of the corresponding solution (the adaptivity value Fiti).
(1) In the process of artificial hivegion algorithm, first, initialization, including determining the number of populations, maximum iterative number MCN,, control parameter LIMIT, and determine the scope of the search space, randomly generate initial solutions in search space (i = 1, 2, 3, ..., Sn), SN is the number of food sources, each decomposition XI is a D-Dimensional vector, D is the dimension of the problem. After initialization, the entire population will lead the bee, follow the rest of the bee and detectors, until the maximum number of iterations MCN or error allows value ε.
(2) In the start of the search process, each leading bell is generated by a new solution from the formula (2-3).
Vij = xij + φij (xij-xkj) (2-3)
In the formula, k∈ {1, 2, ..., Sn}, j∈ {1, 2, ..., D}, and K ≠ I; φij is the random number between [-1, 1]. Calculate the new solution and evaluate it, if the new solution is better than the old solution, lead the bees to remember the new solution forget the old solution. Conversely, it will retain the old solution.
(3) After all leading bees completed the search process, leading the bee will jump in the recruitment area and the information and information to follow the bee. Follow the bee according to the probability of the selection of each solution according to the style,
PI = FITI / σK = 1SNFITK. (2-4)
Then, in the range [-1, 1], one number is random, if the probability value of the solution is greater than the random number, follow the intertert to create a new solution by the formula (2-3), and check the new solution of Fiti, if new solution Fiti is better than before, follow the bee to remember the new solution forgetting the old solution; contrary, it will keep the old solution.
Fourth, after all the searches are completed in the pace, if a solution passes the Limit cycle is still not further updated, then this solution is to be trapped into local optimal, and the food source will be discarded. If the food source Xi is abandoned, the bee corresponding to the food source is converted into an investigating bee. The reconnaissance bee produces a new food source for (2-5). [/ font] [/ color] [/ align] xij = xminj + rand (0,1) (xmaxj-xminj) (2-5) [align = left] [color = RGB (0, 0, 0)] [ Font = 0uO0quot] where j∈ {1, 2 ..., D}. Then return to lead the bee search process, start repeating the loop.
V. The quality of the food source of artificial house is generally, the larger, that is, the greater the adaptivity value, and correspond to the problem of optimization, it takes two cases to consider: the minimum problem, maximum problem. Setting Fi is the target function of optimizing the problem, so if the minimum problem is optimized, the adaptivity function is deformed by FI, which is generally represented by the formula (2-6); if the maximum problem, the adaptation function is the target function. [/ font] [/ color] [/ align] [align = left] [color = RGB (0, 0, 0)] [font =&quot] FITI = {1 + ABS (FI) FI> = 01/1 + FI FI> 0 (2-6)
The artificial bee colony algorithm generally performs greed when evaluating food sources. Select in formula (2-7). [/ font] [/ color] [/ align] [align = left] [color = RGB (0, 0, 0)] [font =&quot] VI = xi FIT (xi) <= FIT (VI) VI FIT (vi)> FIT (xi) (2-7)
Artificial bee colony algorithm is through loop search, eventually find the best food source or optimal solution. [/ font] [/ color] [/ align] [align = left] [color = RGB (0, 0, 0)] [font =&quot] 5 algorithm step
Artificial bee group algorithm specific implementation steps:
Step 1: Initializing population: Initialize each parameter, the total number of bee colony SN, the number of food sources is acquired, the maximum number of iterations MCN and the control parameter limit, determine the problem search range, and randomly generate initial solution Xi in the search range (i = 1, 2, ... SN).
Step 2: Calculate and evaluate the adaptation of each initial solution.
Step 3: Set the cycle condition and start cycling
Step 4: Lead the bell to solve the XI to generate neighborhood (2-3) to generate new solution (food source) Vi, and calculate its adaptivity value;
Step 5: Follow the formula (2-7): If the adaptivity value of the VI is better than xi, use the VI replacement Xi, the VI is used as the current best solution, otherwise the xi constant;
Step 6: The probability PI of the food source is calculated according to the formula (2-4);
Step 7: Follow the bee in accordance with the probability PI select solution or food source, and search according to the formula (2-3) to produce a new solution (food source) Vi, and calculate its adaptation.
Step 8: Press the equation (2-7) to perform greedy selection; if the adaptation of VI is better than XI, use VI instead of Xi, the VI is the best solution, otherwise the xi constant is retained;
Step 9: Judging whether there is a solution to abandon. If there is, the detection bee is randomly generated by the formula (2-5).
Step 10: Record the optimal solution so far;
Step 11: Determine if the loop termination condition is satisfied, if the cycle is completed, the optimal solution is output, otherwise, returns to step 4 to continue search. [/ font] [/ color] [/ align] [B] Second, source code [/ b] Global Radar1global radar2global r% radar1 = [350 105 305 105 175 245 415 480 40 470];% baili = size (Radar1, 2);% RADAR2 = [200 0 -150 110 110 110 0 110 100 -50];% r = [140 70 150 30 25 25 25 90 40 30]; [B] Third, operation results [/ b] [align = Left] [color = RGB (0, 0)] [font =&quot] [IMG] https://img-blog.csdnimg.cn/2020123024627555.png?x-oss-process=image/watermark,ty_zmFuz3Pozw5nagvpdgk , shadow_10, text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1RJUUNtYXRsYWI =, size_16, color_FFFFFF, t_70 [/ img] [/ font] [/ color] [/ align] IV Notes [/ b] [align = left] [color = rgb (0, 0 , 0)] [Font = 0uO0quot] Version: 2014A [/ font] [/ color] [/ align] [align = left] [font = pingfang sc, helvetica neue, helvetica, arial, sans-serif] [color = # 000000] Complete code or writing plus QQ1564658423 [/ color] [/ font] [/ align]