Showing papers in "International Journal of Applied Metaheuristic Computing in 2010"
TL;DR: A review of the developments of Harmony Search during the past decade is given and a rigorous analysis of this approach is performed to compare it to the well-known search heuristic called Evolution Strategies.
Abstract: In recent years a lot of novel (mostly naturally inspired) search heuristics have been proposed. Among those approaches is Harmony Search. After its introduction in 2000, positive results and improvements over existing approaches have been reported. In this paper, the authors give a review of the developments of Harmony Search during the past decade and perform a rigorous analysis of this approach. This paper compares Harmony Search to the well-known search heuristic called Evolution Strategies. Harmony Search is a special case of Evolution Strategies in which the authors give compelling evidence for the thesis that research in Harmony is fundamentally misguided. The overarching question is how such a method could be inaccurately portrayed as a significant innovation without confronting a respectable challenge of its content or credentials. The authors examine possible answers to this question, and implications for evaluating other procedures by disclosing the way in which limitations of the method have been systematically overlooked.
142 citations
TL;DR: This study investigates whether learning heuristic selection can improve the performance of a great deluge based hyper-heuristic using an examination timetabling problem as a case study.
Abstract: Hyper-heuristics can be identified as methodologies that search the space generated by a finite set of low level heuristics for solving search problems. An iterative hyper-heuristic framework can be thought of as requiring a single candidate solution and multiple perturbation low level heuristics. An initially generated complete solution goes through two successive processes heuristic selection and move acceptance until a set of termination criteria is satisfied. A motivating goal of hyper-heuristic research is to create automated techniques that are applicable to a wide range of problems with different characteristics. Some previous studies show that different combinations of heuristic selection and move acceptance as hyper-heuristic components might yield different performances. This study investigates whether learning heuristic selection can improve the performance of a great deluge based hyper-heuristic using an examination timetabling problem as a case study.
137 citations
TL;DR: A survey on the application of Evolutionary Algorithms to Instance Selection and Generation process will cover approaches applied to the enhancement of the nearest neighbor rule, as well as other approaches focused on the improvement of the models extracted by some well-known data mining algorithms.
Abstract: The use of Evolutionary Algorithms to perform data reduction tasks has become an effective approach to improve the performance of data mining algorithms. Many proposals in the literature have shown that Evolutionary Algorithms obtain excellent results in their application as Instance Selection and Instance Generation procedures. The purpose of this paper is to present a survey on the application of Evolutionary Algorithms to Instance Selection and Generation process. It will cover approaches applied to the enhancement of the nearest neighbor rule, as well as other approaches focused on the improvement of the models extracted by some well-known data mining algorithms. Furthermore, some proposals developed to tackle two emerging problems in data mining, Scaling Up and Imbalance Data Sets, also are reviewed.
72 citations
TL;DR: Three movement strategies are discussed for multi-objective PSO (MOPSO) and popular test problems are used to confirm their effectiveness and results show that the algorithms are effective with both direct and indirect encoding schemes.
Abstract: Particle Swarm Optimization (PSO) is one of the most effective metaheuristics algorithms, with many successful real-world applications. The reason for the success of PSO is the movement behavior, which allows the swarm to effectively explore the search space. Unfortunately, the original PSO algorithm is only suitable for single objective optimization problems. In this paper, three movement strategies are discussed for multi-objective PSO (MOPSO) and popular test problems are used to confirm their effectiveness. In addition, these algorithms are also applied to solve the engineering design and portfolio optimization problems. Results show that the algorithms are effective with both direct and indirect encoding schemes.
44 citations
TL;DR: This paper proposes a new multiagent algorithm based on multiple trajectory searches and saving the optima found to use when a change is detected in the environment, which shows the efficiency of the proposed algorithm, even in multimodal environments.
Abstract: Many real-world problems are dynamic and require an optimization algorithm that is able to continuously track a changing optimum over time. In this paper, a new multiagent algorithm is proposed to solve dynamic problems. This algorithm is based on multiple trajectory searches and saving the optima found to use them when a change is detected in the environment. The proposed algorithm is analyzed using the Moving Peaks Benchmark, and its performances are compared to competing dynamic optimization algorithms on several instances of this benchmark. The obtained results show the efficiency of the proposed algorithm, even in multimodal environments.
41 citations
TL;DR: A meta heuristic computational model for a generic cognitive process of human problem solving is developed and helps to explain the cognitive and collective intelligent foundations of metaheuristic computing and its engineering applications.
Abstract: In studies of genetic algorithms, evolutionary computing, and ant colony mechanisms, it is recognized that the higher-order forms of collective intelligence play an important role in metaheuristic computing and computational intelligence. Collective intelligence is an integration of collective behaviors of individuals in social groups or collective functions of components in computational intelligent systems. This paper presents the properties of collective intelligence and their applications in metaheuristic computing. A social psychological perspective on collected intelligence is elaborated toward the studies on the structure, organization, operation, and development of collective intelligence. The collective behaviors underpinning collective intelligence in groups and societies are analyzed via the fundamental phenomenon of the basic human needs. A key question on how collective intelligence is constrained by social environment and group settings is explained by a formal motivation/attitude-driven behavioral model. Then, a metaheuristic computational model for a generic cognitive process of human problem solving is developed. This work helps to explain the cognitive and collective intelligent foundations of metaheuristic computing and its engineering applications.
26 citations
TL;DR: This paper shows how to improve approaches by new inequalities that dominate those previously proposed and by associated target objectives that underlie the creation of both inequalities and trial solutions and proposes procedures for generating target objectives and solutions by exploiting proximity in original space or projected space.
Abstract: Recent adaptive memory and evolutionary metaheuristics for mixed integer programming have included proposals for introducing inequalities and target objectives to guide the search. These guidance approaches are useful in intensification and diversification strategies related to fixing subsets of variables at particular values, and in strategies that use linear programming to generate trial solutions whose variables are induced to receive integer values. In Part I the present paper, we show how to improve such approaches by new inequalities that dominate those previously proposed and by associated target objectives that underlie the creation of both inequalities and trial solutions. Part I focuses on exploiting inequalities in target solution strategies by including partial vectors and more general target objectives. We also propose procedures for generating target objectives and solutions by exploiting proximity in original space or projected space. Part II of this study to appear in a subsequent issue focuses on supplementary linear programming models that exploit the new inequalities for intensification and diversification, and introduce additional inequalities from sets of elite solutions that enlarge the scope of these models. Part II indicates more advanced approaches for generating the target objective based on exploiting the mutually reinforcing notions of reaction and resistance. Our work in the concluding segment, building on the foundation laid in Part I, examines ways our framework can be exploited in generating target objectives, employing both older adaptive memory ideas of tabu search and newer ones proposed here for the first time.
26 citations
TL;DR: The proposed methodology consists of three main phases and each phase has several steps in which activities that must be carried out are clearly defined in this paper.
Abstract: Metaheuristic algorithms will gain more and more popularity in the future as optimization problems are increasing in size and complexity. In order to record experiences and allow project to be replicated, a standard process as a methodology for designing and implementing metaheuristic algorithms is necessary. To the best of the authors' knowledge, no methodology has been proposed in literature for this purpose. This paper presents a Design and Implementation Methodology for Metaheuristic Algorithms, named DIMMA. The proposed methodology consists of three main phases and each phase has several steps in which activities that must be carried out are clearly defined in this paper. In addition, design and implementation of tabu search metaheuristic for travelling salesman problem is done as a case study to illustrate applicability of DIMMA.
25 citations
TL;DR: A team of Finite Learning Automata is introduced, combined with the random walk algorithm, using Boolean satisfiability encoding for the GCP, and an experimental analysis of the new algorithm's performance is presented.
Abstract: The graph coloring problem (GCP) is a widely studied combinatorial optimization problem due to its numerous applications in many areas, including time tabling, frequency assignment, and register allocation. The need for more efficient algorithms has led to the development of several GC solvers. In this paper, the authors introduce a team of Finite Learning Automata, combined with the random walk algorithm, using Boolean satisfiability encoding for the GCP. The authors present an experimental analysis of the new algorithm's performance compared to the random walk technique, using a benchmark set containing SAT-encoding graph coloring test sets.
25 citations
TL;DR: A series of theorems based on partial derivatives that can be readily adopted to form the essential part of r-flip heuristic search methods for Pseudo-Boolean optimization are presented.
Abstract: Modern metaheuristic methodologies rely on well defined neighborhood structures and efficient means for evaluating potential moves within these structures. Move mechanisms range in complexity from simple 1-flip procedures where binary variables are "flipped" one at a time, to more expensive, but more powerful, r-flip approaches where "r" variables are simultaneously flipped. These multi-exchange neighborhood search strategies have proven to be effective approaches for solving a variety of combinatorial optimization problems. In this paper, we present a series of theorems based on partial derivatives that can be readily adopted to form the essential part of r-flip heuristic search methods for Pseudo-Boolean optimization. To illustrate the use of these results, we present preliminary results obtained from four simple heuristics designed to solve a set of Max 3-SAT problems.
21 citations
TL;DR: The authors propose a technique to derive reversible or quantum circuits from BDDs by substituting all nodes of the BDD with a cascade of Toffoli or quantum gates, respectively.
Abstract: Reversible logic became a promising alternative to traditional circuits because of its applications in emerging technologies such as quantum computing, low-power design, DNA computing, or nanotechnologies. As a result, synthesis of the respective circuits is an intensely studied topic. However, most synthesis methods are limited, because they rely on a truth table representation of the function to be synthesized. In this paper, the authors present a synthesis approach that is based on Binary Decision Diagrams (BDDs). The authors propose a technique to derive reversible or quantum circuits from BDDs by substituting all nodes of the BDD with a cascade of Toffoli or quantum gates, respectively. Boolean functions containing more than a hundred of variables can efficiently be synthesized. More precisely, a circuit can be obtained from a given BDD using an algorithm with linear worst case behavior regarding run-time and space requirements. Furthermore, using the proposed approach, theoretical results known from BDDs can be transferred to reversible circuits. Experiments show better results (with respect to the circuit cost) and a significantly better scalability in comparison to previous synthesis approaches.
TL;DR: This paper is written to rebut the original paper's claims by saying 1) harmony search is different from evolution strategies because each has its own uniqueness, 2) performance, rather than novelty, is an algorithm's survival factor, and 3) theOriginal paper was biased to mislead into a predefined conclusion.
Abstract: Recently a paper was published which claims "harmony search is equivalent to evolution strategies and because the latter is not popular currently, the former has no future. Also, research community was misguided by the former's disguised novelty." This paper is written to rebut the original paper's claims by saying 1) harmony search is different from evolution strategies because each has its own uniqueness, 2) performance, rather than novelty, is an algorithm's survival factor, and 3) the original paper was biased to mislead into a predefined conclusion." Also, the shortcomings of current review system, citation system, and funding system are briefly mentioned.
TL;DR: This paper develops a more advanced approach for generating the target objective based on exploiting the mutually reinforcing notions of reaction and resistance and demonstrates how to produce new inequalities by "mining" reference sets of elite solutions.
Abstract: Recent metaheuristics for mixed integer programming have included proposals for introducing inequalities and target objectives to guide this search. These guidance approaches are useful in intensification and diversification strategies related to fixing subsets of variables at particular values. The authors' preceding Part I study demonstrated how to improve such approaches by new inequalities that dominate those previously proposed. In Part II, the authors review the fundamental concepts underlying weighted pseudo cuts for generating guiding inequalities, including the use of target objective strategies. Building on these foundations, this paper develops a more advanced approach for generating the target objective based on exploiting the mutually reinforcing notions of reaction and resistance. The authors demonstrate how to produce new inequalities by "mining" reference sets of elite solutions to extract characteristics these solutions exhibit in common. Additionally, a model embedded memory is integrated to provide a range of recency and frequency memory structures for achieving goals associated with short term and long term solution strategies. Finally, supplementary linear programming models that exploit the new inequalities for intensification and diversification are proposed.
TL;DR: In this article, the authors present an experimental investigation of tabu search (TS) to solve the 3-coloring problem (3-COL) and show that a basic TS algorithm is able to find proper 3-colorings for random 3-colourable graphs with up to 11000 vertices and beyond when instances follow the uniform or equipartite well-known models, and up to 1500 vertices for the hardest class of flat graphs.
Abstract: The authors present an experimental investigation of tabu search (TS) to solve the 3-coloring problem (3-COL). Computational results reveal that a basic TS algorithm is able to find proper 3-colorings for random 3-colorable graphs with up to 11000 vertices and beyond when instances follow the uniform or equipartite well-known models, and up to 1500 vertices for the hardest class of flat graphs. This study also validates and reinforces some existing phase transition thresholds for 3-COL.
TL;DR: The Multi-objective Particle Swarm Optimization technique is adopted to optimize performances of the embryonic cell forming the modulator, that is, a class AB grounded gate switched current memory cell to design the switched current sigma delta modulator.
Abstract: This paper presents the optimal design of a switched current sigma delta modulator. The Multi-objective Particle Swarm Optimization technique is adopted to optimize performances of the embryonic cell forming the modulator, that is, a class AB grounded gate switched current memory cell. The embryonic cell was optimized regarding to its main performances such as sampling frequency and signal to noise ratio. The optimized memory cell was used to design the switched current modulator which operates at a 100 MHz sampling frequency and the output signal spectrum presents a 45.75 dB signal to noise ratio.
TL;DR: A hybrid genetic approach is presented for the two-dimensional rectangular guillotine oriented cutting-stock problem where the genetic algorithm is used to select a set of cutting patterns while the linear programming model permits one to create the lengths to produce with each cutting pattern to fulfil the customer orders with minimal production cost.
Abstract: In this paper, the authors present a hybrid genetic approach for the two-dimensional rectangular guillotine oriented cutting-stock problem. In this method, the genetic algorithm is used to select a set of cutting patterns while the linear programming model permits one to create the lengths to produce with each cutting pattern to fulfil the customer orders with minimal production cost. The effectiveness of the hybrid genetic approach has been evaluated through a set of instances which are both randomly generated and collected from the literature.
TL;DR: In this paper, simple and general Order-Up-To (OUT) models with Minimum Mean Square Error (MMSE) forecast for the AR(1) demand pattern are introduced in the control engineering perspective and important insights about lead-time misidentification are derived from the analysis of variance discrepancy.
Abstract: In this paper, simple and general Order-Up-To (OUT) models with Minimum Mean Square Error (MMSE) forecast for the AR(1) demand pattern are introduced in the control engineering perspective. Important insights about lead-time misidentification are derived from the analysis of variance discrepancy. By applying the Final Value Theorem (FVI), a final value offset (i.e., inventory drift) is proved to exist and can be measured even though the actual lead-time is known. In this regard, to eliminate the inherent offset and keep the system variances acceptable, two kinds of zero inventory drift variants based on the general OUT model are presented. The analysis of variance amplification suggests lead-times should always be estimated conservatively in variant models. The stability conditions for zero inventory drift variants are evaluated in succession and some valuable attributes of the new variants are illustrated via spreadsheet simulation under the assumption that lead-time misidentification is inevitable.
TL;DR: The proposed algorithm based on ant colony optimization metaheuristic is applied to many classes of graphs, and the results obtained have proven satisfactory when compared to those of the existing methods in the literature.
Abstract: In graph theory, a graceful labeling of a graph G = (V, E) with n vertices and m edges is a labeling of its vertices with distinct integers between 0 and m inclusive, such that each edge is uniquely identified by the absolute difference between its endpoints. In this paper, the well-known graceful labeling problem of graphs is represented as an optimization problem, and an algorithm based on Ant Colony Optimization metaheuristic is proposed for finding its solutions. In this regard, the proposed algorithm is applied to different classes of graphs and the results are compared with the few existing methods inside of different literature.
TL;DR: The authors present a local search algorithm based on the well-known tabu search metaheuristic for two dimensional strip packing problem, where a finite set of objects have to be located in a strip of fixed width and infinite height.
Abstract: This paper discusses a particular "packing" problem, namely the two dimensional strip packing problem, where a finite set of objects have to be located in a strip of fixed width and infinite height. The variant studied considers regular items, rectangular to be precise, that must be packed without overlap, not allowing rotations. The objective is to minimize the height of the resulting packing. In this regard, the authors present a local search algorithm based on the well-known tabu search metaheuristic. Two important components of the presented tabu search strategy are reinforced in attempting to include problem knowledge. The fitness function incorporates a measure related to the empty spaces, while the diversification relies on a set of historically "frozen" objects. The resulting reinforced tabu search approach is evaluated on a set of well-known hard benchmark instances and compared with state-of-the-art algorithms.
TL;DR: Results show that PN(G)=TW(G), which proves a conjecture of Ganley and Heath showing that some known upper bounds for the pagenumber can be improved, which is the minimum number of pages in a book-embedding of G.
Abstract: Book-embedding of graph G involves embedding its vertices along the spine of the book and assigning its edges to pages of the book such that no two edges cross on the same page The pagenumber of G is the minimum number of pages in a book-embedding of G In this paper, the authors also examine the treewidth TW(G), which is the minimum k such that G is a subgraph of a k-tree The authors then study the relationship between pagenumber and treewidth Results show that PN(G)=TW(G), which proves a conjecture of Ganley and Heath showing that some known upper bounds for the pagenumber can be improved