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Dialectics of Nature: Inspiration for Computing
Published in Nazmul Siddique, Hojjat Adeli, Nature-Inspired Computing, 2017
Many methods have emerged for the solution of optimization problems of this kind, which can be divided into two categories based on the produced solutions (Weise et al., 2009), namely, deterministic algorithms and nondeterministic (stochastic) algorithms as shown in Figure 1.3. Deterministic algorithms in general follow more rigorous procedures repeating the same path every time and providing the same solution in different runs. Most conventional or classic algorithms are deterministic and based on mathematical programming. Many different mathematical programming methods have been developed in the past few decades. Examples of deterministic algorithms are linear programming (LP), convex programming, integer programming, quadratic programming, dynamic programming, nonlinear programming (NLP), and gradient-based (GB) and gradient-free (GF) methods. These methods usually provide accurate solutions for problems in continuous space. Most of these methods, however, need the gradient information of the objective function, constraints, and a suitable initial point.
State of the Art in Optimal Design and Control of Urban Wastewater Systems
Published in Carlos Alberto Vélez Quintero, Optimization of Urban Wastewater Systems using Model Based Design and Control, 2020
Deterministic algorithms will always produce the same results when given the same inputs. For many problems however, deterministic algorithms are unfeasible. In global optimization, the problem space is often extremely large and the relation of an element’s structure and its utility as solution is not obvious (Weise 2009). In the optimization of UWwS the relation between an alternative solution and its objective functions is very complex and the dimensionality of the search space is very high. Therefore to solve the MOP deterministically is highly complicated. Trying it would possibly result in exhaustive enumeration of the search space, which is not feasible even for relatively small problems.
Cuckoo Search Algorithm, Glowworm Algorithm, WASP, and Fish Swarm Optimization
Published in Anand Nayyar, Dac-Nhuong Le, Nhu Gia Nguyen, Advances in Swarm Intelligence for Optimizing Problems in Computer Science, 2018
A simple way toward classifying optimization algorithms is to split them into two categories: deterministic and stochastic. Deterministic algorithms are non-random, predictable algorithms by nature, which given an initial input always produce the same output with the same sequence of steps. However, these are found to be insufficient for many real-world problems. Stochastic algorithms, on the other hand, search the solution space with a certain degree of randomness in their searching technique.
A modified camel travelling behaviour algorithm for engineering applications
Published in Australian Journal of Electrical and Electronics Engineering, 2019
Ramzy S. Ali, Falih M. Alnahwi, Abdulkareem S. Abdullah
Life has been a source of revelation for developing many optimisation algorithms that can be classified into deterministic and stochastic algorithms. Deterministic algorithms are those that have predictable behaviour and will always produce the same output if given a particular input (Yang 2010a). The stochastic algorithms can be divided into heuristic and metaheuristic algorithms, where their difference is slight. Heuristic algorithms find solution in acceptable amount of time, but there is no guarantee that the best solution can be found; it means no such optimality. On the other hand, ‘beyond’ or ‘higher level’ and they generally perform better than simple heuristic (Arora and Singh 2013). In addition, all metaheuristic algorithms use certain trade-off of randomisation and local search.
Determining the Natural Frequency of Cantilever Beams Using ANN and Heuristic Search
Published in Applied Artificial Intelligence, 2018
Mehdi Nikoo, Marijana Hadzima-Nyarko, Emmanuel Karlo Nyarko, Mohammad Nikoo
Optimization algorithms are basically iterative in nature and as such the quality of an optimization algorithm is determined by the quality of the result obtained in a finite amount of time. Deterministic algorithms are designed in such a way that the optimal solution is always found in a finite amount of time. Thus, deterministic algorithms can only be implemented in situations where the search space can efficiently be explored. Deterministic optimization algorithms also depend on the initial value(s) of the parameter(s) being optimized. In situations, where the search space cannot be efficiently explored, e.g., a high dimensional search space, implementing a deterministic algorithm might result in an exhaustive search, which would be unfeasible due to time constraint. In order to overcome this problem, heuristic optimization methods are often implemented. Heuristic optimization methods generally optimize a problem by iteratively trying to improve a candidate solution with respect to a given measure of quality. They make few or no assumptions about the problem being optimized and can search very large spaces of candidate solutions. However, probabilistic algorithms provide no guarantee of an optimal solution being found, only a good solution in a finite amount of time.