Solving combinatorial optimization problems is an important challenge in all engineering applicat... more Solving combinatorial optimization problems is an important challenge in all engineering applications. Researchers have been extensively solving these problems using evolutionary computations. This paper introduces a novel learning-based multi-agent system (LBMAS) in which all agents cooperate by acting on a common population and a two-stage archive containing promising fitness-based and positional-based solutions found so far. Metaheuristics as agents perform their own method individually and then share their outcomes. This way, even though individual performance may be low, collaboration of metaheuristics leads the system to reach high performance. In this system, solutions are modified by all running metaheuristics and the system learns gradually how promising metaheuristics are, in order to apply them based on their effectiveness. Finally, the performance of LBMAS is experimentally evaluated on Multiprocessor Scheduling Problem (MSP) which is an outstanding combinatorial optimization problem. Obtained results in comparison to well-known competitors show that our multi-agent system achieves better results in reasonable running times.
The 2003 Congress on Evolutionary Computation, 2003. CEC '03.
Abstract-A genetic algorithm employing multiple crossover operators in its implementation is pres... more Abstract-A genetic algorithm employing multiple crossover operators in its implementation is presented. A set of available crossover operators is established initially and a particular crossover operator is probabilistically selected from this set for recombination. Each crossover ...
2013 IEEE 14th International Symposium on Computational Intelligence and Informatics (CINTI), 2013
ABSTRACT Multiprocessor scheduling problem is one of the most important issues regarding to paral... more ABSTRACT Multiprocessor scheduling problem is one of the most important issues regarding to parallel programming and distributed system environments. Multiprocessor scheduling is known as a NP-hard problem, hence, applying an exact solution method is not recommended at all. Single-objective type of multiprocessor scheduling problem has already been solved by evolutionary algorithms like genetic algorithms, ant colony optimization, particle swarm optimization, mean field annealing and so on. This paper presents a mean field annealing approach for solving the multi-objective type of this problem. We introduce multi-objective multiprocessor scheduling problem with three objectives and then solve it using mean field annealing approach. Finally, the proposed algorithm is tested over some benchmarks and its effectiveness is compared to NSGA2 and MOGA algorithms. Obtained results show that mean field annealing method leads better Pareto fronts within reasonable computation times.
Ant Colony Optimization and Swarm Intelligence, 2004
Abstract. An ant colony optimization algorithm using a library of par-tial solutions for knowledg... more Abstract. An ant colony optimization algorithm using a library of par-tial solutions for knowledge incorporation from previous iterations is in-troduced. Initially, classical ant colony optimization algorithm runs for a small number of iterations and the library of partial solutions is ini- ...
Computer and Information Sciences - ISCIS 2003, 2003
Abstract. A novel multi-deme parallel genetic algorithm approach that eliminates the use of the s... more Abstract. A novel multi-deme parallel genetic algorithm approach that eliminates the use of the selection operator by using multiple popula-tions separated into two societies is introduced. Each individual popula-tion contains two subpopulations, one in each society, and ...
A two-stage memory architecture and search operators exploiting the accumulated experience in mem... more A two-stage memory architecture and search operators exploiting the accumulated experience in memory are maintained within the framework of a Great DeLuge algorithm for real-valued global optimization. The level-based acceptance criterion of the Great DeLuge algorithm is applied for each best solution extracted in a particular iteration. The use of memory-based search supported by effective move operators results in a powerful optimization algorithm. The success of the presented approach is illustrated using three sets of well-known benchmark functions including problems of varying sizes and difficulties. Performance of the presented approach is evaluated and in comparison to well-known algorithms and their published results. Except for a few large-scale optimization problems, experimental evaluations demonstrated that the presented approach performs at least as good as its competitors.
Abstract Ant colony optimization algorithm is integrated with an external memory for the purpose ... more Abstract Ant colony optimization algorithm is integrated with an external memory for the purpose of improving its effi-ciency for the solution of a well-known hard combinato-rial optimization problem. The external memory keeps variable-size solution segments extracted from ...
The 2003 Congress on Evolutionary Computation, 2003. CEC '03.
Abstract A novel evolutionary assignment-ordering approach for combinatorial optimization using c... more Abstract A novel evolutionary assignment-ordering approach for combinatorial optimization using constraint satisfaction problem (CSP) modelling is presented. In assignment of values to variables, the order of assignment is determined by an ordering function combined with ...
The 2003 Congress on Evolutionary Computation, 2003. CEC '03.
Abstract-An Ant Colony Optimization approach to-wards the development of robust game strategies f... more Abstract-An Ant Colony Optimization approach to-wards the development of robust game strategies for iterated prisoner's dilemma (IPD) is presented. . It is demonstrated that, anb can play-IPD and, in spite of comparably longer running times, provide game strate-gies of better ...
Abstract-Reciprocal translocation is a nature-inspired crossover operator that is proved to be a ... more Abstract-Reciprocal translocation is a nature-inspired crossover operator that is proved to be a useful al-ternative for the conventional multi-point and uniform crossover operators. This paper compares the per-formance of reciprocal translocation against the well-known ...
Multiobjective real parameter optimization is a challenging problem in majority of engineering ap... more Multiobjective real parameter optimization is a challenging problem in majority of engineering applications. This paper presents a creative multiagent and dynamic multi-deme architecture based on a novel collaboration mechanism for the solution of multiobjective real-parameter optimization problems. The proposed architecture comprises a number of multiobjective metaheuristic agents that act on subsets of a population based on a cyclic assignment order. The proposed multiagent architecture works iteratively in sessions including two consecutive phases: in the first phase, a population of solutions is divided into subpopulations based on dominance ranks of its elements. In the second phase, each multiobjective metaheuristic is assigned to work on a subpopulation based on a cyclic or round-robin order. Hence, each metaheuristic operates on a different-rank subpopulation in subsequent sessions, where a session starts with a new assignment of metaheuristics and ends when termination criteria for the session are satisfied. Individual agents have their local archives of non-dominated solutions extracted in a session, while there is a global archive keeping all non-dominated solutions found so far. At the end of each session, all subpopulations are combined into one global population to be used for the initialization of the next session. Similarly, all local archives are merged with the global archive to get the set of all non-dominated solutions found by all metaheuristics through working on subsets of different rank-levels. This way, the metaheuristics cooperate with each other by sharing their search experiences through collecting them in a common population and a common global archive. The proposed multiagent system is experimentally evaluated using the well-known CEC2009 multiobjective optimization benchmark problems set. Analysis of the experimental results demonstrated that the proposed architecture achieves better performance compared to majority of its state-of-the-art competitors in almost all problem instances.
During the recent decade, segmentation algorithms have been applied extensively to medical images... more During the recent decade, segmentation algorithms have been applied extensively to medical images in order to assist physician treating and diagnosing variety of diseases. Among all types of medical images, X-ray imaging is one of the frequently used imaging methods, especially for diagnosing bone diseases. Great numbers of segmentation methods have been applied on X-ray images aiming to segment bones with higher accuracy; however, it is hard to find a single method that is capable of segmenting all body parts with equal level of quality. Therefore, researchers put significant efforts on the combination different methods or improvement of existing methods using some techniques to achieve a desired segmentation outcome. In this research, improvement and refinement methods are proposed for three well-known clustering algorithms, namely the K-means, fuzzy c-means and spatial fuzzy c-means algorithms. The proposed methods include a novel entropy guided improvement strategy for clusterin...
International Journal on Artificial Intelligence Tools, 2021
This research work proposes a dynamic metaheuristic network that is a layered interconnection of ... more This research work proposes a dynamic metaheuristic network that is a layered interconnection of a number of multi-objective optimization (MOO) algorithms. Each node of the network corresponds to a MOO metaheuristic and interconnections between the nodes represent the flow of subpopulation elements in a feed-forward direction. The proposed method runs in consecutive sessions such that a session starts with the assignment of subpopulations to each of the individual nodes, proceeds with execution of node metaheuristics within their algorithmic framework and ends with feeding the improved subpopulations to the connected forward nodes. The network architecture is dynamic in the sense that nodes change their layers at the end of each session. At the end of each session, elements of the improved subpopulations are fed forward to nodes in subsequent layers which update their own subpopulations using uniform random sampling. The proposed method is evaluated on CEC2009, ZDT, DTLZ, WFG benchmarks and several real-world MOO problems using the experimental framework described for these problem instances. Comparative evaluations against a large set of state-of-the-art algorithms exhibited that the proposed method with its novel dynamic network architecture and subpopulation assignment strategy is promising both in the quality of the extracted Pareto fronts and in leading future research on ensembles of MOO algorithms.
Solving combinatorial optimization problems is an important challenge in all engineering applicat... more Solving combinatorial optimization problems is an important challenge in all engineering applications. Researchers have been extensively solving these problems using evolutionary computations. This paper introduces a novel learning-based multi-agent system (LBMAS) in which all agents cooperate by acting on a common population and a two-stage archive containing promising fitness-based and positional-based solutions found so far. Metaheuristics as agents perform their own method individually and then share their outcomes. This way, even though individual performance may be low, collaboration of metaheuristics leads the system to reach high performance. In this system, solutions are modified by all running metaheuristics and the system learns gradually how promising metaheuristics are, in order to apply them based on their effectiveness. Finally, the performance of LBMAS is experimentally evaluated on Multiprocessor Scheduling Problem (MSP) which is an outstanding combinatorial optimization problem. Obtained results in comparison to well-known competitors show that our multi-agent system achieves better results in reasonable running times.
The 2003 Congress on Evolutionary Computation, 2003. CEC '03.
Abstract-A genetic algorithm employing multiple crossover operators in its implementation is pres... more Abstract-A genetic algorithm employing multiple crossover operators in its implementation is presented. A set of available crossover operators is established initially and a particular crossover operator is probabilistically selected from this set for recombination. Each crossover ...
2013 IEEE 14th International Symposium on Computational Intelligence and Informatics (CINTI), 2013
ABSTRACT Multiprocessor scheduling problem is one of the most important issues regarding to paral... more ABSTRACT Multiprocessor scheduling problem is one of the most important issues regarding to parallel programming and distributed system environments. Multiprocessor scheduling is known as a NP-hard problem, hence, applying an exact solution method is not recommended at all. Single-objective type of multiprocessor scheduling problem has already been solved by evolutionary algorithms like genetic algorithms, ant colony optimization, particle swarm optimization, mean field annealing and so on. This paper presents a mean field annealing approach for solving the multi-objective type of this problem. We introduce multi-objective multiprocessor scheduling problem with three objectives and then solve it using mean field annealing approach. Finally, the proposed algorithm is tested over some benchmarks and its effectiveness is compared to NSGA2 and MOGA algorithms. Obtained results show that mean field annealing method leads better Pareto fronts within reasonable computation times.
Ant Colony Optimization and Swarm Intelligence, 2004
Abstract. An ant colony optimization algorithm using a library of par-tial solutions for knowledg... more Abstract. An ant colony optimization algorithm using a library of par-tial solutions for knowledge incorporation from previous iterations is in-troduced. Initially, classical ant colony optimization algorithm runs for a small number of iterations and the library of partial solutions is ini- ...
Computer and Information Sciences - ISCIS 2003, 2003
Abstract. A novel multi-deme parallel genetic algorithm approach that eliminates the use of the s... more Abstract. A novel multi-deme parallel genetic algorithm approach that eliminates the use of the selection operator by using multiple popula-tions separated into two societies is introduced. Each individual popula-tion contains two subpopulations, one in each society, and ...
A two-stage memory architecture and search operators exploiting the accumulated experience in mem... more A two-stage memory architecture and search operators exploiting the accumulated experience in memory are maintained within the framework of a Great DeLuge algorithm for real-valued global optimization. The level-based acceptance criterion of the Great DeLuge algorithm is applied for each best solution extracted in a particular iteration. The use of memory-based search supported by effective move operators results in a powerful optimization algorithm. The success of the presented approach is illustrated using three sets of well-known benchmark functions including problems of varying sizes and difficulties. Performance of the presented approach is evaluated and in comparison to well-known algorithms and their published results. Except for a few large-scale optimization problems, experimental evaluations demonstrated that the presented approach performs at least as good as its competitors.
Abstract Ant colony optimization algorithm is integrated with an external memory for the purpose ... more Abstract Ant colony optimization algorithm is integrated with an external memory for the purpose of improving its effi-ciency for the solution of a well-known hard combinato-rial optimization problem. The external memory keeps variable-size solution segments extracted from ...
The 2003 Congress on Evolutionary Computation, 2003. CEC '03.
Abstract A novel evolutionary assignment-ordering approach for combinatorial optimization using c... more Abstract A novel evolutionary assignment-ordering approach for combinatorial optimization using constraint satisfaction problem (CSP) modelling is presented. In assignment of values to variables, the order of assignment is determined by an ordering function combined with ...
The 2003 Congress on Evolutionary Computation, 2003. CEC '03.
Abstract-An Ant Colony Optimization approach to-wards the development of robust game strategies f... more Abstract-An Ant Colony Optimization approach to-wards the development of robust game strategies for iterated prisoner's dilemma (IPD) is presented. . It is demonstrated that, anb can play-IPD and, in spite of comparably longer running times, provide game strate-gies of better ...
Abstract-Reciprocal translocation is a nature-inspired crossover operator that is proved to be a ... more Abstract-Reciprocal translocation is a nature-inspired crossover operator that is proved to be a useful al-ternative for the conventional multi-point and uniform crossover operators. This paper compares the per-formance of reciprocal translocation against the well-known ...
Multiobjective real parameter optimization is a challenging problem in majority of engineering ap... more Multiobjective real parameter optimization is a challenging problem in majority of engineering applications. This paper presents a creative multiagent and dynamic multi-deme architecture based on a novel collaboration mechanism for the solution of multiobjective real-parameter optimization problems. The proposed architecture comprises a number of multiobjective metaheuristic agents that act on subsets of a population based on a cyclic assignment order. The proposed multiagent architecture works iteratively in sessions including two consecutive phases: in the first phase, a population of solutions is divided into subpopulations based on dominance ranks of its elements. In the second phase, each multiobjective metaheuristic is assigned to work on a subpopulation based on a cyclic or round-robin order. Hence, each metaheuristic operates on a different-rank subpopulation in subsequent sessions, where a session starts with a new assignment of metaheuristics and ends when termination criteria for the session are satisfied. Individual agents have their local archives of non-dominated solutions extracted in a session, while there is a global archive keeping all non-dominated solutions found so far. At the end of each session, all subpopulations are combined into one global population to be used for the initialization of the next session. Similarly, all local archives are merged with the global archive to get the set of all non-dominated solutions found by all metaheuristics through working on subsets of different rank-levels. This way, the metaheuristics cooperate with each other by sharing their search experiences through collecting them in a common population and a common global archive. The proposed multiagent system is experimentally evaluated using the well-known CEC2009 multiobjective optimization benchmark problems set. Analysis of the experimental results demonstrated that the proposed architecture achieves better performance compared to majority of its state-of-the-art competitors in almost all problem instances.
During the recent decade, segmentation algorithms have been applied extensively to medical images... more During the recent decade, segmentation algorithms have been applied extensively to medical images in order to assist physician treating and diagnosing variety of diseases. Among all types of medical images, X-ray imaging is one of the frequently used imaging methods, especially for diagnosing bone diseases. Great numbers of segmentation methods have been applied on X-ray images aiming to segment bones with higher accuracy; however, it is hard to find a single method that is capable of segmenting all body parts with equal level of quality. Therefore, researchers put significant efforts on the combination different methods or improvement of existing methods using some techniques to achieve a desired segmentation outcome. In this research, improvement and refinement methods are proposed for three well-known clustering algorithms, namely the K-means, fuzzy c-means and spatial fuzzy c-means algorithms. The proposed methods include a novel entropy guided improvement strategy for clusterin...
International Journal on Artificial Intelligence Tools, 2021
This research work proposes a dynamic metaheuristic network that is a layered interconnection of ... more This research work proposes a dynamic metaheuristic network that is a layered interconnection of a number of multi-objective optimization (MOO) algorithms. Each node of the network corresponds to a MOO metaheuristic and interconnections between the nodes represent the flow of subpopulation elements in a feed-forward direction. The proposed method runs in consecutive sessions such that a session starts with the assignment of subpopulations to each of the individual nodes, proceeds with execution of node metaheuristics within their algorithmic framework and ends with feeding the improved subpopulations to the connected forward nodes. The network architecture is dynamic in the sense that nodes change their layers at the end of each session. At the end of each session, elements of the improved subpopulations are fed forward to nodes in subsequent layers which update their own subpopulations using uniform random sampling. The proposed method is evaluated on CEC2009, ZDT, DTLZ, WFG benchmarks and several real-world MOO problems using the experimental framework described for these problem instances. Comparative evaluations against a large set of state-of-the-art algorithms exhibited that the proposed method with its novel dynamic network architecture and subpopulation assignment strategy is promising both in the quality of the extracted Pareto fronts and in leading future research on ensembles of MOO algorithms.
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