A cooperative group optimization (CGO) system is presented to implement CGO cases by integrating the advantages of the cooperative group and low-level algorithm portfolio design. Following the nature-inspired paradigm of a cooperative group, the agents not only explore in a parallel way with their individual memory, but also cooperate with their peers through the group memory. Each agent holds a portfolio of (heterogeneous) embedded search heuristics (ESHs), in which each ESH can drive the group into a stand-alone CGO case, and hybrid CGO cases in an algorithmic space can be defined by low-level cooperative search among a portfolio of ESHs through customized memory sharing. The optimization process might also be facilitated by a passive group leader through encoding knowledge in the search landscape. Based on a concrete framework, CGO cases are defined by a script assembling over instances of algorithmic components in a toolbox. A multilayer design of the script, with the support of the inherent updatable graph in the memory protocol, enables a simple way to address the challenge of accumulating heterogeneous ESHs and defining customized portfolios without any additional code. The CGO system is implemented for solving the constrained optimization problem with some generic components and only a few domain-specific components. Guided by the insights from algorithm portfolio design, customized CGO cases based on basic search operators can achieve competitive performance over existing algorithms as compared on a set of commonly-used benchmark instances. This work might provide a basic step toward a user-oriented development framework, since the algorithmic space might be easily evolved by accumulating competent ESHs.
It is also interesting to discuss about the similarities and differences with existing multimethod @cite_66 , multi-operator @cite_62 @cite_43 @cite_44 , and ensemble methods @cite_46 @cite_76 @cite_57 . These algorithms provides high-performance realizations in combining the strengths of different search operators (e.g., ES, GA, DE, and PSO) and constraint-handling techniques. These operators might be significant sources for the CGO toolbox. Furthermore, a distinguished feature of these algorithms is that multiple operators compete the offspring generation in sub-populations with adaptively varied sizes, to favor some operators that exhibit higher reproducive success, over the evolution process. Compared to these methods, the CGO system provide a flexible way to support customized low-level cooperation among heterogeneous ESHs that interact on multiple chunks in the private memory of individual agents.
@cite_62 @cite_44 @cite_43 @cite_57 @cite_46 @cite_76 @cite_66
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Many different algorithms have been developed in the last few decades for solving complex real-world search and optimization problems. The main focus in this research has been on the development of a single universal genetic operator for population evolution that is always efficient for a diverse set of optimization problems. In this paper, we argue that significant advances to the field of evolutionary computation can be made if we embrace a concept of self-adaptive multimethod optimization in which multiple different search algorithms are run concurrently, and learn from each other through information exchange using a common population of points. We present an evolutionary algorithm, entitled A Multialgorithm Genetically Adaptive Method for Single Objective Optimization (AMALGAM-SO), that implements this concept of self adaptive multimethod search. This method simultaneously merges the strengths of the covariance matrix adaptation (CMA) evolution strategy, genetic algorithm (GA), and particle swarm optimizer (PSO) for population evolution and implements a self-adaptive learning strategy to automatically tune the number of offspring these three individual algorithms are allowed to contribute during each generation. Benchmark results in 10, 30, and 50 dimensions using synthetic functions from the special session on real-parameter optimization of CEC 2005 show that AMALGAM-SO obtains similar efficiencies as existing algorithms on relatively simple unimodal problems, but is superior for more complex higher dimensional multimodal optimization problems. The new search method scales well with increasing number of dimensions, converges in the close proximity of the global minimum for functions with noise induced multimodality, and is designed to take full advantage of the power of distributed computer networks. Over the last two decades, many sophisticated evolutionary algorithms have been introduced for solving constrained optimization problems. Due to the variability of characteristics in different COPs, no single algorithm performs consistently over a range of problems. In this paper, for a better coverage of the problem characteristics, we introduce an algorithm framework that uses multiple search operators in each generation. The appropriate mix of the search operators, for any given problem, is determined adaptively. The framework is tested by implementing two different algorithms. The performance of the algorithms is judged by solving 60 test instances taken from two constrained optimization benchmark sets from specialized literature. The first algorithm, which is a multi-operator based genetic algorithm (GA), shows a significant improvement over different versions of GA (each with a single one of these operators). The second algorithm, using differential evolution (DE), also confirms the benefit of the multi-operator algorithm by providing better and consistent solutions. The overall results demonstrated that both GA and DE based algorithms show competitive, if not better, performance as compared to the state of the art algorithms. Abstract This paper proposes a push and pull search (PPS) framework for solving constrained multi-objective optimization problems (CMOPs). To be more specific, the proposed PPS divides the search process into two different stages: push and pull search stages. In the push stage, a multi-objective evolutionary algorithm (MOEA) is used to explore the search space without considering any constraints, which can help to get across infeasible regions very quickly and to approach the unconstrained Pareto front. Furthermore, the landscape of CMOPs with constraints can be probed and estimated in the push stage, which can be utilized to conduct the parameter setting for the constraint-handling approaches to be applied in the pull stage. Then, a modified form of a constrained multi-objective evolutionary algorithm (CMOEA), with improved epsilon constraint-handling, is applied to pull the infeasible individuals achieved in the push stage to the feasible and non-dominated regions. To evaluate the performance regarding convergence and diversity, a set of benchmark CMOPs and a real-world optimization problem are used to test the proposed PPS (PPS-MOEA D) and state-of-the-art CMOEAs, including MOEA D-IEpsilon, MOEA D-Epsilon, MOEA D-CDP, MOEA D-SR, C-MOEA D and NSGA-II-CDP. The comprehensive experimental results show that the proposed PPS-MOEA D achieves significantly better performance than the other six CMOEAs on most of the tested problems, which indicates the superiority of the proposed PPS method for solving CMOPs. This paper proposes an evolutionary-group-based particle-swarm-optimization (EGPSO) algorithm for fuzzy-controller (FC) design. The EGPSO uses a group-based framework to incorporate crossover and mutation operations into particle-swarm optimization. The EGPSO dynamically forms different groups to select parents in crossover operations, particle updates, and replacements. An adaptive velocity-mutated operation (AVMO) is incorporated to improve search ability. The EGPSO is applied to design all of the free parameters in a zero-order Takagi-Sugeno-Kang (TSK)-type FC. The objective of EGPSO is to improve fuzzy-control accuracy and design efficiency. Comparisons with different population-based optimizations of fuzzy-control problems demonstrate the superiority of EGPSO performance. In particular, the EGPSO-designed FC is applied to mobile-robot navigation in unknown environments. In this application, the robot learns to follow object boundaries through an EGPSO-designed FC. A simple learning environment is created to build this behavior without an exhaustive collection of input-output training pairs in advance. A behavior supervisor is proposed to combine the boundary-following behavior and the target-seeking behavior for navigation, and the problem of dead cycles is considered. Successful mobile-robot navigation in simulation and real environments verifies the EGPSO-designed FC-navigation approach.