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In this paper, we present a hierarchicity-based (self-similar) hybrid genetic algorithm for the solution of the grey pattern quadratic assignment problem. This is a novel hybrid genetic search-based heuristic algorithm with the or...
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In this paper, we present a hierarchicity-based (self-similar) hybrid genetic algorithm for the solution of the grey pattern quadratic assignment problem. This is a novel hybrid genetic search-based heuristic algorithm with the original, hierarchical architecture and it is in connection with what is known as self-similarity-this means that an object (in our case, algorithm) is exactly or approximately similar to constituent parts of itself. The two main aspects of the proposed algorithm are the following: (1) the hierarchical (self-similar) structure of the genetic algorithm itself, and (2) the hierarchical (self-similar) form of the iterated tabu search algorithm, which is integrated into the genetic algorithm as an efficient local optimizer (local improvement algorithm) of the offspring solutions produced by the crossover operator of the genetic algorithm.
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This paper proposes a novel metaheuristic optimizer, named Colony Search Optimization Algorithm (CSOA). The algorithm mimics the social behavior of early humans. Early humans expanded their settlements in search of more livable pl...
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This paper proposes a novel metaheuristic optimizer, named Colony Search Optimization Algorithm (CSOA). The algorithm mimics the social behavior of early humans. Early humans expanded their settlements in search of more livable places to live. In CSOA, the worst solution is used to escape from local optima. And the number of these redundant solutions' updates is reduced to improve the performance of the algorithm. CSOA is tested with 26 mathematical optimization problems and 4 classical engineering optimization problems. The optimization results are compared with those of various optimization algorithms. The experimental results show that the CSOA is able to provide very competitive results on most of the tested problems. Then, a new effective method is provided for solving optimization problems.
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Solving hard problems like the Travelling Salesman Problem (TSP) is a major challenge faced by analysts even though many techniques are available. The main goal of TSP is that a number of cities should be visited by a salesman and...
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Solving hard problems like the Travelling Salesman Problem (TSP) is a major challenge faced by analysts even though many techniques are available. The main goal of TSP is that a number of cities should be visited by a salesman and return to the starting city along a number of possible shortest paths. TSP is although looking a simple problem, but it is an important problem of the classical optimization problems that are difficult to solve conventionally. It has been proved that solving TSP by the conventional approaches in a reasonable time is not possible. So, the only feasible option left is to use heuristic algorithms. In this article, we apply and investigate a new heuristic approach called Heart algorithm to solve Travelling Salesman Problem. It simulates the heart action and circulatory system procedure in the human beings for searching the problem space. The Heart algorithm has the advantages of strong robustness, fast convergence, fewer setting parameters and simplicity. The results of the Heart algorithm on several standard TSP instances is compared with the results of the PSO and ACO algorithms and show that the Heart algorithm performs well in finding the shortest distance within the minimum span of time.
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Extensive and computationally complex signal processing and control applications are commonly constructed from small computational blocks where the load decomposition and balance may not be easily achieved. This requires the devel...
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Extensive and computationally complex signal processing and control applications are commonly constructed from small computational blocks where the load decomposition and balance may not be easily achieved. This requires the development of mapping and scheduling strategies based on application to processor matching. In this context several application algorithms are utilised and investigated in this work within the development framework (DF) approach. The DF approach supports the specification, design and implementation of real-time control systems. It also contains several mapping and scheduling tools to improve the performance of systems as well as tools for code generation. To improve the performance of an application, a new approach, namely the priority-based genetic algorithm (PBGA), is developed and reported in this article. The approach is applied to several applications using parallel and distributed heterogeneous architectures and its performance verified in comparison to several previously developed strategies.
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This paper proposes a novel nature-inspired algorithm called Multi-Verse Optimizer (MVO). The main inspirations of this algorithm are based on three concepts in cosmology: white hole, black hole, and wormhole. The mathematical mod...
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This paper proposes a novel nature-inspired algorithm called Multi-Verse Optimizer (MVO). The main inspirations of this algorithm are based on three concepts in cosmology: white hole, black hole, and wormhole. The mathematical models of these three concepts are developed to perform exploration, exploitation, and local search, respectively. The MVO algorithm is first benchmarked on 19 challenging test problems. It is then applied to five real engineering problems to further confirm its performance. To validate the results, MVO is compared with four well-known algorithms: Grey Wolf Optimizer, Particle Swarm Optimization, Genetic Algorithm, and Gravitational Search Algorithm. The results prove that the proposed algorithm is able to provide very competitive results and outperforms the best algorithms in the literature on the majority of the test beds. The results of the real case studies also demonstrate the potential of MVO in solving real problems with unknown search spaces. Note that the source codes of the proposed MVO algorithm are publicly available at http://www.alimirjalili.com/MVO.html.
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A common problem faced by organizations is how to select and schedule an optimal portfolio of projects subject to various constraints, such as a limited budget. This problem is known as the project portfolio selection and scheduli...
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A common problem faced by organizations is how to select and schedule an optimal portfolio of projects subject to various constraints, such as a limited budget. This problem is known as the project portfolio selection and scheduling problem (PPSSP). Despite the widespread nature of this problem, no existing model adequately addresses a sufficient set of characteristics that arise in real-world problems. One contribution of this article is the proposal of a novel, practical class of PPSSP that consists of multiple groups of projects, proposed by different sections of a major organization. The proposed problem can be considered as a generalized PPSSP given that many specific PPSSPs reported in the literature can be generated by relaxing certain constraints. As this is a novel formulation, existing algorithms cannot ensure high-quality solutions to this problem. Thus, a further contribution of this article is the design of three hybrid meta-heuristic algorithms based on a custom-purpose heuristic and local search operator. A case problem, inspired by future force design (FFD) in the Australian Defence Force (ADF), is presented to exemplify the applicability of this model to a real-world problem. Results indicate that the obtained solutions are of acceptable quality for implementation.
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The laws of gravity and mass interactions inspire the gravitational search algorithm (GSA), which finds optimal regions of complex search spaces through the interaction of individuals in a population of particles. Although GSA has...
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The laws of gravity and mass interactions inspire the gravitational search algorithm (GSA), which finds optimal regions of complex search spaces through the interaction of individuals in a population of particles. Although GSA has proven effective in both science and engineering, it is still easy to suffer from premature convergence especially facing complex problems. In this paper, we proposed a new hybrid algorithm by integrating genetic algorithm (GA) and GSA (GA-GSA) to avoid premature convergence and to improve the search ability of GSA. In GA-GSA, crossover and mutation operators are introduced from GA to GSA for jumping out of the local optima. To demonstrate the search ability of the proposed GA-GSA, 23 complex benchmark test functions were employed, including unimodal and multimodal high-dimensional test functions as well as multimodal ' test functions with fixed dimensions. Wilcoxon signed-rank tests were also utilized to execute statistical analysis of the results obtained by PSO, GSA, and GA-GSA. Experimental results demonstrated that the proposed algorithm is both efficient and effective.
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We introduce BubbleSearch, a general approach for extending priority-based greedy heuristics. Following the framework recently developed by Borodin et al., we consider priority algorithms, which sequentially assign values to eleme...
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We introduce BubbleSearch, a general approach for extending priority-based greedy heuristics. Following the framework recently developed by Borodin et al., we consider priority algorithms, which sequentially assign values to elements in some fixed or adaptively determined order. BubbleSearch extends priority algorithms by selectively considering additional orders near an initial good ordering. While many notions of nearness are possible, we explore algorithms based on the Kendall-tau distance (also known as the BubbleSort distance) between permutations. Our contribution is to elucidate the BubbleSearch paradigm and experimentally demonstrate its effectiveness.
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Selection hyper-heuristics are a technology for optimization in which a high-level mechanism controls low-level heuristics, so as to be capable of solving a wide range of problem instances efficiently. Hyper-heuristics are used to...
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Selection hyper-heuristics are a technology for optimization in which a high-level mechanism controls low-level heuristics, so as to be capable of solving a wide range of problem instances efficiently. Hyper-heuristics are used to generate a solution process rather than producing an immediate solution to a given problem. This process is a re-usable mechanism that can be applied both to seen and unseen problem instances. In this paper, we propose a selection hyper-heuristic process with the intention to rise the level of generality and solve consistently well a wide range of constraint satisfaction problems. The hyper-heuristic technique is based on a messy genetic algorithm that generates high-level heuristics formed by rules (condition heuristic). The high-level heuristics produced are seen to be good at solving instances from certain parts of the parameterized space of problems, producing results using effort comparable to the best single heuristic per instance. This is beneficial, as the choice of best heuristic varies from instance to instance, so the high-level heuristics are definitely preferable to selecting any one low-level heuristic for all instances. The results confirm the robustness of the proposed approach and how high-level heuristics trained for some specific classes of instances can also be applied to unseen classes without significant lost of efficiency. This paper contributes to the understanding of heuristics and the way they can be used in a collaborative way to benefit from their combined strengths.
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A greedy randomized adaptive search procedure (GRASP) is a metaheuristic for combinatorialoptimization. It is a multi-start or iterative process, in which each GRASP iteration consists of twophases, a construction phase, in which ...
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A greedy randomized adaptive search procedure (GRASP) is a metaheuristic for combinatorialoptimization. It is a multi-start or iterative process, in which each GRASP iteration consists of twophases, a construction phase, in which a feasible solution is produced, and a local search phase, in which alocal optimum in the neighborhood of the constructed solution is sought. Since 1989, numerous papers onthe basic aspects of GRASP, as well as enhancements to the basic metaheuristic have appeared in theliterature. GRASP has been applied to a wide range of combinatorial optimization problems, ranging fromscheduling and routing to drawing and turbine balancing. This is the first of two papers with an annotatedbibliography of the GRASP literature from 1989 to 2008. This paper covers algorithmic aspects of GRASP.
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