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Heuristic Algorithm & Meta-heuristic Algorithm

Abstract

This project is coding for fun. My goal is exerting to clarify the code and making the structure to be clear and strong. I also want to learn the OOP concept. If you have any suggestion, feel free to contact me. Thx! Have fun.

Btw, this is the only public project, so if you are interested in other projects, send me correspondence.

To execute the code

  • Please use shell file, named run . sh

Fast link

Problems

One-max problem

  • Def : There is a vector that saves "0" or "1". The length of the vector is defined by user.
  • Goal : The goal of one-max problem is to find "1" as many as possible in the vector.

Deception problem

  • Def : A function which is defined as $f(x_{bin}) = |x_{dec} - 2 ^ {(n - 2)}|$
    The $x$ means a vector that has been input. The $x_{bin}$ means $x$ using binary to show. The $x_{dec}$ means $x$ using decimal to show. The $n$ means the length of $x_{bin}$.
  • Goal : The goal is making the function result as huge as possible.

Traveling Salesman Problem

  • Def : There are some cities which are allocated on a map. A salesman wants to find an efficient way to travel.
  • Goal : The goal is trying to find a route which is the shortest.

Algorithms

Exhaustive Search (1)

Trying to search the possible answers as many as possible.

  • Pros : No missing any possible solution.
  • Cons : Wasting the computing capability. If possible solutions are unlimited, couldn't find the best answer anyway.
  • EX :
    • The solution starts from zero. It will be added by one based on decimal. The maximum number depends on user's input. It will be 2^number_of_input.

Hill Climbing (2)

Finding a better fitness solution nearby current one, and move toward to it.

  • Pros : Can find a better solution nearby current one.

  • Cons : It might easily trap into a local optimum.

  • EX :

    • There are two ways of transition. One is randomly pick a position to change, and another one is only check the left or right position based on decimal.

Simulated Annealing (3)

Giving some probabilities to accept worse solution for searching other possible areas.

  • Pros : Can find a better solution if it traps in a local optimum.

  • Cons : It might easily trap into a local optimum.

  • EX :

    • There are two ways of transition. One is randomly pick a position to change, and another one is only check the left or right position based on decimal.
    • The initial temperature is set to 100 or by user input. Temperature will decrease by every iteration.
    • The anneal energy is calculated by exp((current_fitness - latest_fitness) / current_temperature)

Tabu Search (4)

Trying to search a possible solution nearby the certain one which is better and not contained in the tabu list. TS will generate the tabu list to save some solutions at the beginning.

  • Pros : To avoid a place where has been visited.

  • Cons : The size of the tabu list might be not enough.

  • EX :

    • The original tabu list is set to size 7.
    • The tabu list will record the space where has been visited and avoid these areas, so it has more chances to search the new areas that have not been searched.

Genetic Algorithm (5)

Genetic Algorithm is called GA. It simulates the process of evolution. Each solution must go through selection, crossover and mutation. The selection is for picking better ancients. The crossover is for combining different ancients to make offsprings be diverse. The mutation is for mutating the offspring to make the offspring slightly different.

  • Pros : Due to the process of evolution, the solution will be diverse, so it might easily reach a better solution.

  • Cons : Loading might be heavy.

  • EX :

    • Roulette selection is referring to the fitness of each solution. Every solution has own proportion based on its fitness value.
    • Tournament selection is to randomly pick two solution, and compete the fitness of each other. Keep the better one into next generation.
    • Crossover can be implemented by many ways. The one-point crossover has been used here.

Ant Colony Optimization (6)

Ant colony Optimization is called ACO. It mimics the behaviors of ants. When ants pass somewhere, they put the pheromones on a route. The rest of ants will follow strong odor. Basically, when a route has been visited many times, the odor will be stronger. Finally, many ants will follow this strong pheromones route. The steps is choosing a city, update pheromones in local, and update pheromones in global.

  • Pros : The best route can be found.

  • Cons : Loading is heavy, and parameters are sensitive.

  • EX :

    • An ant will choose a promising city depending on selecting strong pheromones equation. The paper can be found by Dorigo in 1997.
    • The pheromones table will be updated after local search and global search.
    • The best route will significantly add strong pheromones on the map.

Particle Swarm Optimization (7)

Particle Swarm Optimization is called PSO. It mimics the behaviors of birds. Birds will gather together. The transition is referring to the best particle and the best history of each particle.

  • Pros : Having strong power.

  • Cons : A little bit hard to find the exact solution.

  • EX :

    • It uses velocity, the best of each particle in history, and the global best.

Modified Commits

  • 07/31/2021 The commit should be "Fixed bugs. Formatted. The OOP concept".
  • BACK TO the TOP

Best,

Henry Chuang