Whether the sensitivity analysis for the problem in the meta heuristic algorithm is meaningful

The meta-heuristic algorithm is proposed relative to the optimization algorithm, the optimization algorithm of a problem can obtain the optimal solution of the problem, and the meta-heuristic algorithm is an algorithm based on intuitive or empirical construction, which can give a feasible solution to the problem at an acceptable cost (referring to the calculation time and space), and the degree of deviation between the feasible solution and the optimal solution may not be predicted in advance. Meta-heuristic algorithms include taboo search algorithm, simulated annealing algorithm, genetic algorithm, ant colony optimization algorithm, particle swarm optimization algorithm, artificial fish swarm algorithm, artificial bee colony algorithm, artificial neural network algorithm, etc.

Encyclopedia] Sensitivity analysis is a method to study and analyze the sensitivity of the state or output changes of a system (or model) to changes in system parameters or surrounding conditions. Sensitivity analysis is often used in optimization methods to study the stability of the optimal solution when the original data is inaccurate or changes. Sensitivity analysis can also be used to determine which parameters have a greater impact on the system or model.

Therefore, sensitivity analysis is important in almost all operations research methods, as well as in the evaluation of various programmes.

From the explanation of these two concepts, I think that the sensitivity analysis for the problem in the meta-heuristic algorithm does not make sense.

My understanding is that the meta-heuristic algorithm is based on the problem modeling to obtain the optimal solution, and the mathematical model suitable for the meta-heuristic algorithm to solve is generally to seek the optimal objective function under certain constraints, so this solution process is to traverse a variety of possibilities under a certain computational cost, the influence of the parameters has been traversed, and there is no need to conduct sensitivity analysis of the influence of the trial parameters, which I think is redundant.

Nature is magical, and it has created a lot of ingenious means and operating mechanisms. Inspired by nature, people have found many solutions to practical problems from the laws of nature. For those methods that are inspired by the laws of nature or the experience and rules of specific problems, people often call them heuristics

algorithm）。Today's heuristics are not all derived from natural laws, but also from the accumulated work experience of human beings.

Driving a car to someone's home, the algorithm is written like this: Take Highway 167 south to Yanggu; Take the Yanggu Expressway exit and drive up the hill for miles; Turn right at the traffic light next to a grocery store, then turn left at the first one; Enter from the driveway of the big brown house on the left, and it is someone's house.

The heuristic way to describe it might be as follows: find the last letter we sent you, and drive to the town according to the address on the letter; When you arrive, you ask where our house is. Everybody here knows us – and there's sure someone will be willing to help you; If you can't find anyone, then find a public kiosk and we'll come out and pick you up.

Hello Heuristic:

The two fundamental goals of computer science are to discover algorithms that can be proven to perform well and to produce the best or second best solution. Heuristics, on the other hand, try to provide one or all of the targets at once. For example, it can often find a good solution, but there is no way to prove that it won't get a bad solution; It usually solves the answer in a reasonable amount of time, but there's no way to know if it solves at this speed every time.

Sometimes people find that heuristics get very bad answers or are very inefficient in some special cases, but the data structures that create those special cases may never appear in the real world. Therefore, heuristics are commonly used in the real world to solve problems. Heuristics can often get good answers in a reasonable amount of time when dealing with many real-world problems.

There is a class of general heuristic strategies called metaheuristics, which usually use random number search techniques. They can be applied to a very wide range of problems, but efficiency is not guaranteed.

Finally, as the name suggests, heuristic scheduling algorithms are heuristics used in the process of scheduling.

Wangha: Thank you

Sorry, I can only answer the second question. It is generally believed that there is no guarantee that the global optimal solution will be found using meta-heuristic algorithms, and if you want to prove that the solution found is the global optimal solution, there are two ways you can try: first, use mathematical methods to prove that it is the global optimal solution (equal to lower bound), and second, arrange all possible solutions, if there is no better solution than the one you get, then the solution is the global optimal solution.

Sensitivity = number of true positives (number of true positives + number of false negatives) * 100%. Correctly judge the patient's rate.

Specificity = number of true negatives (number of true negatives + number of false positives))) * 100%. Correctly judge the rate of non-patients.

Diagnostic ultrasound total.

a b c d

Sensitivity A(a+C)*100%.

Specificity d (b+d)*100%.

Clinical specificity is a measure of the ability of a test to correctly determine a person who is disease-free, and specificity is the proportion of people who are actually disease-free correctly as a true negative.

Sensitivity, also known as true positive rate, is the percentage of people who are actually sick and are correctly judged to be sick according to the criteria of this diagnostic test. It reflects the ability of diagnostic tests to find patients. Specificity, also known as true negative rate, is the percentage of people who are actually disease-free and correctly judged by diagnostic tests to be disease-free.

It reflects the ability of diagnostic tests to identify non-patients. Sensitivity = number of true positives (number of true positives + number of false negatives) * 100%. Correctly judge the patient's rate.

Specificity = number of true negatives (number of true negatives + number of false positives))) * 100%. Correctly judge the rate of non-patients. The importance of sensitivity and specificity metrics.

If the sensitivity of a diagnostic test is low, there will be many false-negative patients. This can delay patient care, affect the course of the disease and procure, and even lead to premature death. If the specificity of a diagnostic test is low, many false-positive patients will appear.

This wastes medical resources and causes unwarranted panic and anxiety among patients. Therefore, high sensitivity and specificity are the basis for the application of a diagnostic test.

The frequency response of the sensor is the ability of the sensor to respond to external signals. For example, if the frequency response of the sensor is 5,000 times per second and the frequency of the external signal is 6,000 times, then the sensor will not be able to react correctly to the external signal. If the sensor is collected as a counter signal, the sensor will not be able to output the correct counter value, resulting in the loss of the counter signal.

Check it out here! Hope that helps!

This is good. Global and local searches. At present, the most common and influential global search algorithms mainly include master-slave algorithm, single-surface algorithm, level domain algorithm, bitcode algorithm and NBS algorithm. Local contact search algorithms are mainly based on:"Point-and-face algorithm", based on"Ball algorithm", based on smooth surface (curve) algorithm. At present, the contact interface algorithm mainly includes the Lass multiplier method and the penalty function method, as well as the perturbation Rasonly method and the extended Rasonly method.

In addition, parallel computation of contact problems is also a research content that cannot be ignored.

Personally, the feeling is the same, the local mountain climbing, the taboo feeling are all a kind of greedy choice, and they are all strategies to help individuals get one step closer to the optimal solution.

There is no way to do this lingo's algorithm can only do sensitivity analysis for general linear programming, and you can cancel the integer condition and do it, which has some reference meaning.