Chaotic Meta-heuristic Algorithms for Optimal by Ali Kaveh (.PDF)

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Chaotic Meta-heuristic Algorithms for Optimal Design of Structures by Ali Kaveh, Hossein Yousefpoor
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Overview: In this book, various chaos maps are embedded in eleven efficient and well-known metaheuristics and a significant improvement in the optimization results is achieved. The two basic steps of metaheuristic algorithms consist of exploration and exploitation. The imbalance between these stages causes serious problems for metaheuristic algorithms, which are immature convergence and stopping in local optima. Chaos maps with chaotic jumps can save algorithms from being trapped in local optima and lead to convergence toward global optima. Embedding these maps in the exploration phase, exploitation phase, or both simultaneously corresponds to three efficient and useful scenarios. By creating competition between different modes and increasing diversity in the search space and creating sudden jumps in the search phase, improvements are achieved for chaotic algorithms. Four Chaotic Algorithms, including Chaotic Cyclical Parthenogenesis Algorithm, Chaotic Water Evaporation Optimization, Chaotic Tug-of-War Optimization, and Chaotic Thermal Exchange Optimization are developed.

The evolution of living organisms is the first choice of researchers for inspiration in meta-heuristic algorithms. In genetic evolution, the characteristics of living organisms are improved by cross-over and mutation so that they can win in competition with other organisms by adapting to the surrounding environment as much as possible. Examples of these algorithms include Genetic Algorithm (GA), Differential Evolution (DE) and Evolutionary Strategy (ES). In the algorithms of the second group, the source of inspiration is based on the Swarm Intelligence of animals and their behavior in foraging. In this group of algorithms, components such as population, cooperation, communication, information exchange, information flow and self-organization are considered. Examples of this group include Ant Colony Optimization (ACO), Cyclical Parthenogenesis Algorithm (CPA), Particle Swarm Optimization (PSO) [3], Cuckoo Search (CS), Artificial Bee Colony (ABC), and Gray Wolf Optimization (GWO), which have achieved valuable results in optimizing structural skeletons in their performance. Another group of meta-heuristic algorithms includes inspiration from physical laws. Examples of these algorithms are: Charged System Search (CSS), Colliding Bodies Optimization (CBO), Water Evaporation Optimization (WEO), Vibrating Particle System (VPS), Big Bang-Big Crunch (BB-BC), Ray Optimization (RO) and Harmony Search (HS). In recent years, these algorithms have been chosen on a large scale for the optimization of structures. They are very popular among researchers.
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