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Nature-Inspired Algorithms: A Comprehensive Review
Published in Siddhartha Bhattacharyya, Václav Snášel, Indrajit Pan, Debashis De, Hybrid Computational Intelligence, 2019
Essam H. Houssein, Mina Younan, Aboul Ella Hassanien
Current research trends in the metaheuristic algorithms focus on identifying new ideas for solving complex problems, and also on inventing new advancement of algorithms for solving new problems or for enhancing performance of existing algorithms. Our aim in this review is to guide researchers to gain better knowledge about latest metaheuristic algorithms which cover a large-scale from the real world problems. Different sources of inspiration produce different types of metaheuristic algorithms. This review briefly summarizes all natural-inspired algorithms according to the sources of inspiration. They could be categorized into: swarm-intelligence-based algorithms (SI), bio-inspired algorithms (BI), nature science-based algorithms (NS), and natural phenomena-based algorithms (NPA). It is worth pointing out that there is no unique classification for the metaheuristic algorithms, and this try is just for the purpose of information only.
AGV Routing via Ant Colony Optimization Using C#
Published in Kaushik Kumar, J. Paulo Davim, Optimization Using Evolutionary Algorithms and Metaheuristics, 2019
Şahin Inanç, Arzu Eren Şenaras
In the early 1990s, ant colony optimization (ACO) was introduced by M. Dorigo and colleagues as a novel, nature-inspired metaheuristic for the solution of hard combinatorial optimization (CO) problems. ACO belongs to the class of metaheuristics, which are approximate algorithms used to obtain good-enough solutions to hard CO problems in a reasonable amount of computation time. Other examples of metaheuristics are tabu search, simulated annealing, and evolutionary computation. The inspiring source of ACO is the foraging behaviour of real ants. When searching for food, ants initially explore the area surrounding their nest in a random manner. As soon as an ant finds a food source, it evaluates the quantity and the quality of the food and carries some of it back to the nest. During the return trip, the ant deposits a chemical pheromone trail on the ground. The quantity of pheromone deposited, which may depend on the quantity and quality of the food, will guide other ants to the food source. As it has been shown in, indirect communication between the ants via pheromone trails enables them to find the shortest paths between their nest and food sources. This characteristic of real ant colonies is exploited in artificial ant colonies in order to solve CO problems (Dorigo and Blum 2005).
Introduction to Swarm Intelligence
Published in Anand Nayyar, Dac-Nhuong Le, Nhu Gia Nguyen, Advances in Swarm Intelligence for Optimizing Problems in Computer Science, 2018
Various algorithms have been proposed on the basis of metaheuristics such as: simulated annealing (SA), tabu search (TS), the memetic algorithm (MA), the artificial immune system (AIS), ant colony optimization (ACO), genetic algorithms (GA), particle swarm optimization (PSO), and differential evolution (DE), which are utilized by researchers for optimizing and finding solutions to various problems of computer science. A metaheuristic algorithm is regarded as an iterative process that lays paths and innovates operations of sub-heuristics to define efficient solutions. It manipulates a complete or incomplete single solution or set of solutions at each iteration. Today, the scope of metaheuristics has widened, and various methods have been proposed such as: ant colony systems, greedy randomized adaptive search, cuckoo search, genetic algorithms, differential evolution, bee algorithms, particle swarm optimization (PSO), etc.
An Integrated Energy Control System to Provide Optimum Demand Side Management of a Grid-Interactive Microgrid
Published in Electric Power Components and Systems, 2023
Izviye Fatimanur Tepe, Erdal Irmak
Although all algorithms in Table 2 can be applied to a wide range of optimization problems, each has its own unique characteristics and strengths. One of the key differences between these algorithms is the way in which they explore the search space in order to find good solutions. GA and BA use a combination of exploration and exploitation to search for solutions, while PSO and ABC rely more on social interactions between potential solutions. ACO use a decentralized approach, in which individual solutions communicate with each other through a process of pheromone deposition and evaporation. GWO and DA use a more aggressive approach, with a focus on global exploration. Another important difference between these algorithms is the way in which they update the population of potential solutions. GA and BA use a process of reproduction and mutation to evolve the population over time, while PSO and ABC use a combination of local and global exploration to update the population. ACO uses a process of pheromone deposition and evaporation to guide the search, while GWO and DA rely on more aggressive global exploration. Overall, the choice of metaheuristic optimization algorithm will depend on the specific characteristics of the optimization problem being solved, as well as the computational resources and constraints of the problem.
Block SMRT and knapsack optimization-based sequency selector for robust, imperceptible, and payload-efficient color image watermarking for binary watermark
Published in International Journal of Computers and Applications, 2023
Febina Ikbal, Rajamma Gopikakumari
Performance of a watermarking system is highly dependent on parameters and positions used for embedding. Most of the existing watermarking techniques determine their parameters and embedding positions analytically. Watermarking algorithms provide a large parameter space, making it difficult to analytically determine the watermarking parameters, and hence optimization is the solution. Embedding positions and parameters to provide optimized robustness, quality and payload can be achieved through metaheuristic optimization techniques. Travelling Salesman Problem (TSP) is an example of an NP-hard problem where the solution space grows exponentially as the size of the problem increases, rendering an exhaustive search infeasible. Metaheuristic is an iterative approach that intelligently searches for the optimal solution within a search space with all possible solutions. Quality of any solution is defined as a fitness function according to the design objectives. The fitness function in multi-objective optimization problem is evaluated by measuring all objective functions. The solutions chosen per iteration are then ranked according to their measurable objectives using a multi-objective optimization process.
Energy-aware task allocation strategy for multi robot system
Published in International Journal of Modelling and Simulation, 2022
The metaheuristics are optimization techniques. They are defined as high-level strategies that guide specific heuristics to increase their performance, by efficiently exploring the search space [20]. These strategies are based on a dynamic balance between the exploration of the search space and the exploitation of local regions. This balance allows the metaheuristics to identify regions in the search space with high-quality solutions quickly. It also allows it to escape from previously explored regions or from regions that do not have good solutions [20]. The metaheuristics can be classified as single-point-search vs. population-based methods. The single point-based search methods, such as ILS, manipulate a single point during the search to achieve the solution. On the other hand, the population-based methods (e.g., particle swarm, genetic algorithm) manipulate a population of solutions. They are exploration-oriented methods.