Intelligent optimization algorithm is a heuristic optimization algorithm, including genetic algorithm, ant colony algorithm, tabu search algorithm, simulated annealing algorithm, particle swarm optimization algorithm and so on. Intelligent optimization algorithms are generally designed for specific problems, with weak theoretical requirements and strong technology. Generally, we will compare intelligent algorithms with optimization algorithms. In contrast, the intelligent algorithm is fast and has strong application.
What are the traditional optimization algorithms and modern optimization algorithms? What is the difference?
1. Traditional optimization algorithms are generally aimed at structural problems, and have a clear description of problems and conditions, such as linear programming, quadratic programming, integer programming, mixed programming, constrained and unconstrained. , that is, clear structural information; However, intelligent optimization algorithms generally describe more general problems and generally lack structured information.
2. Many traditional optimization algorithms belong to the category of convex optimization and have unique and clear global optimization; However, most intelligent optimization algorithms are aimed at multi-extreme problems. How to prevent falling into local optimum and find global optimum as much as possible is the fundamental reason for adopting intelligent optimization algorithm: for unipolar problems, traditional algorithms are good enough most of the time, while intelligent algorithms have no advantage; For multi-extreme problems, intelligent optimization algorithm can find a good balance between jumping out of local optimum and converging to a point through its effective design, so as to find the global optimum, but sometimes the local optimum is acceptable, so the traditional algorithm also has great application space and improvement possibility for special structures.
3. Traditional optimization algorithms are generally deterministic, with fixed structure and parameters, and the computational complexity and convergence can be analyzed theoretically; Most intelligent optimization algorithms are heuristic algorithms, which can be qualitatively analyzed but difficult to quantitatively prove. Moreover, most algorithms are based on random characteristics, and their convergence is generally probabilistic. The actual performance is uncontrollable, and the convergence speed is often slow and the calculation complexity is high.
What is the latest optimization algorithm?
Isn't that too broad? I can't finish a literature review.
Multi-objective optimization algorithm What does multi-objective mean?
The essence of multi-objective optimization is that in most cases, the improvement of one objective may lead to the decline of the performance of other objectives, and it is impossible to optimize multiple objectives at the same time. Only all objectives can be balanced and compromised to make all objective functions as optimal as possible, and the optimal solution of the problem consists of a large number or even infinite Pareto optimal solutions.
Optimization algorithm in programming
1. The process of algorithm optimization is the process of learning and thinking. Learning mathematics is essentially learning thinking. In other words, the purpose of mathematics education is not only to let students master mathematical knowledge (including calculation skills), but more importantly, to let students learn mathematical thinking. The diversity of algorithms has great teaching value. In the process of exploring the diversification of algorithms, students cultivate the flexibility of thinking and develop creativity. While recognizing the diverse teaching value of algorithms, we also realize that the thinking value of different algorithms is not equal. To fully reflect the educational value of algorithm diversification, teachers should actively guide students to optimize the algorithm, regard the process of optimizing the algorithm as another opportunity to develop students' thinking and cultivate students' ability, and turn the optimization algorithm into another learning activity that students actively construct. In the process of optimizing algorithms, let students compare and analyze various algorithms and evaluate them, not just evaluate their correctness-is this right? And evaluate its rationality-does it make sense? Also evaluate its scientific nature-is this the best way? This optimization process is undoubtedly very useful for improving students' thinking quality. In the process of discussion, communication and reflection, students gradually learn the mathematical thinking method of "choosing the best from many, choosing the best from the best". In the process of guiding students to optimize algorithms, teachers help students to sort out their thinking process, summarize their learning methods, develop their thinking habits and form their learning ability. In the long run, students' thinking quality will be greatly improved. 2. Cultivate students' awareness and habit of algorithm optimization in the process of algorithm optimization. Consciousness is a guide to action, and some students show a single state of algorithm because of their inertia of thinking. Obviously, my own algorithm is very complicated, but I don't want to think deeply, just content to work out the result. To improve students' thinking level, we should consciously stimulate the connection between students' thinking and life, help students get rid of the inertia of students' thinking, encourage students to think from multiple angles, and then choose the best scheme; Encourage them not only to focus on their own algorithms, but also to listen carefully to other people's thinking and learn from their strengths; Guide them to feel the connection and rationality of different methods and the unique simplicity of mathematics itself. In the process of re-algorithm optimization, we should not only let students feel the process of refining calculation methods and understand mathematical thinking methods, but more importantly, let students collide with each other to form a calculation method suitable for students' personal reality, so as to cultivate students' mathematical consciousness and enable students to consciously use mathematical thinking methods to analyze things and solve problems. This process is not only to master and consolidate knowledge and skills, but also to make students' thinking more open and profound. 3. Algorithm optimization is a process for students to learn, experience and deepen their understanding. Algorithm diversification is a method put forward by each student through his own independent thinking and exploration, so there are many algorithms in the group. Therefore, the diversity of algorithms is the performance of group learning ability, and it is a collective solution to a problem, not a variety of algorithms for a single student. The optimization of the algorithm is to let students optimize in the process of grouping comparison. By exchanging their own algorithms, students can learn from each other, absorb each other, complement each other, and realize optimization on the premise of individual perception. Because optimization is the process of students' reconstructing knowledge structure, and it is an internal behavior and independent activity of students. However, in the implementation of algorithm optimization teaching, students should be given a certain exploration space and a gradual understanding process. Let students feel in exploration, comparison and choice. In this way, it is beneficial to cultivate students' independent thinking ability and creative ability. 4. Optimization algorithm is also the need of students' further study. Primary school mathematics is the foundation of the whole mathematical system and a subsystem with strict logical relationship. Algorithm teaching is a part of primary school mathematics teaching, not an isolated teaching point. From a certain teaching content, perhaps no algorithm is optimal, but from the whole system of algorithm teaching, there must be a method that is optimal, which students must master in the follow-up study. In the process of algorithm diversification, when students put forward various algorithms, teachers should guide students to make comparative analysis in time, feel the characteristics of different strategies in the process of comparative analysis, understand the reasoning of different methods, analyze the advantages and disadvantages of different methods, and make reasonable evaluation, so as to choose the best method that is universal, simple and conducive to subsequent learning. 5. Optimization is also the driving force of mathematics development. Mathematics is not only a basic discipline, but also a tool discipline, which is widely used. The reason why mathematics is so widely used >>
What are some new intelligent optimization algorithms?
Intelligent optimization algorithm is a heuristic optimization algorithm, including genetic algorithm, ant colony algorithm, tabu search algorithm, simulated annealing algorithm, particle swarm optimization algorithm and so on. Intelligent optimization algorithms are generally designed for specific problems, with weak theoretical requirements and strong technology. Generally, we will compare intelligent algorithms with optimization algorithms.
What are the latest intelligent optimization algorithms? I want to study some new algorithms, and I don't know which ones. ...
A: Ant colony is actually relatively new. What is updated is only the last improvement of these algorithms. There are many evolutionary algorithms. Just search for an article with these titles and read new articles since 2006. It is applicable to all fields. Otherwise, it is the limit and there is no research prospect.
What does it mean to realize function optimization through algorithm?
For example, give a function f (x 1, x2) = x12+x2 2 and find the minimum value of this function. . .
Mathematically, we usually take the partial derivative and pile it up, but in the algorithm, we only need to use gradient descent and several iterations to solve the problem. . .
What are the stopping conditions of the optimization algorithm?
The greater the adaptability, the better the solution.
The method to judge whether the approximate global optimal solution has been obtained is the termination condition of genetic algorithm. Within the maximum number of iterations, you can choose one of the following conditions as the termination condition:
1. The maximum and average fitness values have little change and tend to be stable;
2. The distance between adjacent gap populations is less than the acceptable value, refer to "Jiang Yong, Li Hong. Improved termination criteria of new NSGA protocol [J]. Computer simulation. Volume 26, No.2, 2009
What is the meaning of cell in intelligent optimization algorithm?
Intelligent optimization is mainly used to find the optimal solution, through repeated iterations to find the stable convergence optimal solution or approximate optimal solution, such as finding the maximum value of complex unimodal or multimodal functions.