Immune-based metaheuristic algorithms are a class of optimization algorithms inspired by the human immune system. These algorithms use the principles of immune system function to solve complex optimization problems. The basic idea behind immune-based metaheuristic algorithms is that the immune system mechanisms for detecting and responding to foreign substances in the body can be adapted to solve optimization problems.
The immune system comprises various cells and molecules that protect the body from harmful foreign substances. One of the key components of the immune system is the ability to recognize and respond to antigens, which are substances that the body recognizes as foreign.
The Immune-based metaheuristic algorithms use the principles of antigen-antibody interactions to solve optimization problems. One of the main advantages of immune-based metaheuristic algorithms is their ability to adapt to changes in the problem space. This makes them particularly useful for optimization problems that involve changing environments or uncertain data. Immune-based metaheuristic algorithms are also capable of handling large-scale optimization problems.
The immune system is a complex network of cells, tissues, and organs that work together to defend the body against pathogens, foreign substances, and abnormal cells. Some of the key properties of the immune system are considered as follows,
Specificity:The immune system can recognize and respond to specific antigens, unique molecules found on the surface of pathogens and other foreign substances. The immune system can distinguish between self and non-self molecules, allowing it to target and destroy foreign invaders specifically.
Diversity:The immune system is capable of producing a vast array of different antigen-specific receptors through a process called somatic recombination. This allows the immune system to recognize and respond to a wide range of antigens.
Immunological surveillance:The immune system can recognize and destroy abnormal or mutated cells, including cancer cells. It is known as immunological surveillance.
Memory: After initial exposure to an antigen, the immune system can develop immunological memory, allowing it to mount a faster and more effective response upon subsequent exposures. This is the basis for the effectiveness of vaccines.
Inflammation: The immune system can trigger an inflammatory response to help contain and eliminate infections. Inflammation is characterized by redness, heat, swelling, and pain.
Self-tolerance:The immune system has mechanisms to prevent it from attacking self-antigens, molecules found on the bodys cells. Failure of these mechanisms can lead to autoimmune diseases.
Complement system:The complement system is a set of proteins that work together with antibodies to destroy pathogens. The complement system can also recruit other immune cells to the site of infection.
The immune-based metaheuristic algorithms procedure involves iterative selection, cloning, hypermutation, replacement, and memory cells to find the optimal solution to a given problem. The algorithm starts with an initial population of candidate solutions and iteratively improves the solution fitness values to achieve the problem objective. The algorithm terminates when a stopping criterion is met.
Initialization:The algorithm randomly generates an initial population of candidate solutions. Each solution is represented as a set of values for the problem variables.
Affinity Function: An affinity function is defined to measure the fitness of each solution in the population. The affinity function evaluates each solution and assigns a fitness value based on how well it satisfies the problems objective function.
Selection: Using a selection operator, the algorithm selects the best solutions from the population based on their fitness values. The selected solutions are then cloned to generate more copies of them.
Hypermutation: The cloned solutions are subjected to hypermutation, which is a process that introduces random variations into the solutions to increase their diversity. The somatic hypermutation mechanism inspires the hypermutation process in the immune system, which introduces random mutations into the antibody genes to generate a diverse repertoire of antibodies.
Antibody Pool: The hypermutated solutions are stored in the antibody pool, which represents the diversity of the population.
Clonal Selection:A clonal selection operator selects the best solutions from the antibody pool based on their affinity values. The selected solutions are then cloned to generate more copies of them.
Hypermutation: The cloned solutions are subjected to hypermutation again to increase their diversity further.
Replacement: The newly generated solutions replace the worst solutions in the population using a replacement operator.
Memory Cells:The algorithm maintains a set of memory cells that store the best solutions found so far. The memory cells guide the search towards promising regions of the search space.
Termination:The algorithm terminates when a stopping criterion is met, such as a maximum number of iterations or when the best solutions fitness value meets a predefined threshold.
Clonal Selection Algorithm (CSA):CSA is one of the earliest immune-based metaheuristic algorithms based on the principle of clonal selection, where the fittest antibodies are selected and cloned to generate a new population.
Artificial Immune System (AIS):AIS is a general term for any immune-inspired metaheuristic algorithm. It is a population-based algorithm that uses the principles of the immune system to solve optimization problems.
Negative Selection Algorithm (NSA): NSA is an immune-based metaheuristic algorithm based on the principle of negative selection. In NSA, self-antigens are defined, and the antibodies produced are selected based on their ability to recognize non-self-antigens.
Immune Network Algorithm (INA): INA is created based on the principle that immune networks of antibodies are created, and the interactions between the antibodies are used to solve the optimization problem.
Danger Theory-Based Immune Algorithm (DTIA):DTIA is a type of immune-based algorithm based on the danger theory of the immune system, in which signals in the problem space is used to activate the immune response and search for the optimal solution.
Therefore, each type of immune-based metaheuristic algorithm has its strengths and weaknesses, and the choice of algorithm depends on the problem being solved and the characteristics of the problem space. This algorithm has been successfully applied to various optimization problems in various fields, including engineering, finance, healthcare, and logistics.
Clustering: Applied for clustering in image analysis, bioinformatics, and social network analysis to similar group objects together and identify the underlying patterns in the data.
Resource allocation: Used for resource allocation in healthcare and logistics to optimize the allocation of resources, such as hospital beds or transportation routes, to minimize costs and improve efficiency.
Portfolio optimization:Immune-based metaheuristic algorithms have been used for portfolio optimization in finance. They can identify the optimal investment mix that maximizes returns while minimizing risks.
Engineering:Immune-based metaheuristic algorithms have been applied to various engineering problems, such as antenna design, control system design, and mechanical design optimization.
Image processing:Immune-based metaheuristic algorithms have been applied to image segmentation, registration, and feature selection.
Data mining:Immune-based metaheuristic algorithms have been applied to clustering, classification, and association rule mining.
Transportation:Immune-based metaheuristic algorithms have been applied to various transportation problems, such as route optimization, vehicle routing, and airline scheduling.
Finance:Immune-based metaheuristic algorithms have been applied to financial forecasting, portfolio optimization, and risk management.
Medicine:Immune-based metaheuristic algorithms have been applied to various medical problems, such as drug design, protein structure prediction, and cancer treatment optimization.
