Titlebook: Evolutionary and Swarm Intelligence Algorithms; Jagdish Chand Bansal,Pramod Kumar Singh,Nikhil R. Book 2019 Springer International Publis

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https://doi.org/10.1007/978-3-319-91341-4Computational Intelligence; Evolutionary Algorithms; Swarm Intelligence; Evolutionary Intelligence; Swar

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https://doi.org/10.1007/978-3-031-38801-9e present scenario, involve a variety of decision variables and complex structured objectives, and constraints. Often, the classical or traditional optimization techniques face difficulty in solving such real world optimization problems in their original form. Due to deficiencies of classical optimi

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Western Diet Impact on Multiple Sclerosis,ained, multi-objective and combinatorial type of optimization problems. In the modified ABC algorithm for constrained optimization, the greedy selection mechanism is replaced with Deb’s rules to favor the search towards feasible regions. In the ABC algorithm proposed for multi-objective optimization

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Emission, Reflection, and Dark Nebulae,O) is a global optimization algorithm inspired by Fission-Fusion social (FFS) structure of spider monkeys during their foraging behavior. SMO exquisitely depicts two fundamental concepts of swarm intelligence: self-organization and division of labor. SMO has gained popularity in recent years as a sw

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https://doi.org/10.1007/978-3-476-99073-0hich are otherwise difficult to solve using classical, deterministic techniques. GAs are easier to implement as compared to many classical methods, and have thus attracted extensive attention over the last few decades. However, the inherent randomness of these algorithms often hinders convergence to

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https://doi.org/10.1007/978-3-031-69220-8icit parallel search ability of evolutionary algorithms have made them popular and useful in finding multiple trade-off Pareto-optimal solutions in multi-objective optimization problems since the past two decades. In this chapter, we discuss evolutionary multi-objective optimization (EMO) algorithms

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