Solving Incremental Optimization Problems via Cooperative Coevolution

Cheng, Ran; Omidvar, Mohammad Nabi; Gandomi, Amir H.; Sendhoff, Bernhard; Menzel, Stefan; Yao, Xin

doi:10.1109/tevc.2018.2883599

Cited by 15 publications

(4 citation statements)

References 31 publications

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“…These two populations are adapted accordingly and, after changing the number of objectives, they are reconstructed following mechanisms that consider dimensionality changes. The second type concerns the increase of the number of decision variables [41] that were addressed to single-objective problems.…”

Section: Dynamic Multi-objective Evolutionary Algorithm Designmentioning

confidence: 99%

Dynamic Multi-Objective Evolutionary Algorithms: An Overview

Guerrero-Peña,

Araújo,

Garrozi

2023

Learn. Nonlin. Mod.

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Evolutionary algorithms have been widely explored and applied in optimization problems. The introduction of multi-objective evolutionary algorithms (MOEAs) has facilitated the adaptation and creation of new methods to handle more complex and realistic optimizations, such as dynamic multi-objective optimization problems (DMOPs). A dynamic MOEA (DMOEA) can be constructed by changing the MOEA structure and variation operators used to solve DMOPs. Furthermore, DMOEAs can implement change-detection strategies and mechanisms to handle the dynamics of the environment. DMOEAs are often designed to solve unconstrained DMOPs. However, several studies have been conducted to solve dynamic constrained MOPs and more recently, to solve dynamic many-objective optimization problems. There are many real-world DMOPs; however, few DMOEAs have been evaluated with respect to those problems. Evaluation of algorithm performance is an essential aspect of using DMOEAs. Several measures used to validate MOEAs have been adapted to assess DMOEAs and quantitatively compare them. This paper presents a broad review of DMOEAs. The information is summarized and organized according to current research branches. Furthermore, a more general classification scheme for DMOEAs is proposed, based on the method of incorporating diversity to solve DMOPs.

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Section: Dynamic Multi-objective Evolutionary Algorithm Designmentioning

confidence: 99%

Dynamic Multi-Objective Evolutionary Algorithms: An Overview

Guerrero-Peña,

Araújo,

Garrozi

2023

Learn. Nonlin. Mod.

View full text Add to dashboard Cite

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“…However, such a method discards matching information in previous environments and is not suitable for slightly changing environments. Another method is to optimize the new coming customer requests independently once a change occurs, based on the incremental (INC) optimization method [54], which can reduce response time but may easily fall into local optima. In the customer-EV dispatch service, the environment often changes slightly due to short optimization time.…”

Section: E Strategies Reacting To Dynamic Change Based On Memorymentioning

confidence: 99%

“…We compare MACO with the FCFS approach and five dynamic optimization algorithms, including restart ACS (RSACS), incremental (INC) method-based [54] ACS (INCACS), ACS-DVRP [52], P-ACO [50], and EIACO [53].…”

Section: A Experimental Settingsmentioning

confidence: 99%

Memory-Based Ant Colony System Approach for Multi-Source Data Associated Dynamic Electric Vehicle Dispatch Optimization

Shi

Zhan

Dian-nong

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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The developments of electric vehicle (EV) technology and mobile internet technology have made the EV-oriented ride-hailing service a trend in smart cities. In the service scenario, a high-quality order allocation approach is in great need to quickly process a series of customer request orders, so as to reduce total customer waiting time and transportation cost. To simulate real-world customer-EV allocation scenarios, in this paper, a dynamic EV dispatch (DEVD) model is established by considering multi-source data association from five sources, including customer, vehicle, charging, station, and service. To solve the proposed multi-source data associated DEVD model, a memory-based ant colony optimization (MACO) approach is developed. MACO maintains a memory archive to store the historically good solutions, which not only can be used to update pheromone to guide the search, but also can be used to help the reactions to environmental changes. In response to dynamic changes, a partial reassignment strategy is also proposed to re-optimize some of the assigned customer-EV pairs in the historically best solution. Moreover, an exchange or replace local search procedure is designed to enhance the performance. The MACO algorithm is applied to a set of dynamic test cases with different customer request and EV sizes. Experimental results show that MACO generally outperforms the first-come-firstserved approach and some state-of-the-art ACO-based dynamic optimization algorithms.

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“…The utilization of subjective fitness and complicated dynamics of cooperative coevolution systems are the main reasons for the failure of these systems [282]. Moreover, these systems do not allow mating between the individuals of heterogeneous subpopulations [283,284,285]. In contrast, the novel lateralized system will have an elementary architecture where solutions will be generated by utilizing homogeneous as well as heterogeneous BBKs.…”

Section: Cooperative Coevolutionmentioning

confidence: 99%

Lateralized Learning to Solve Complex Problems

Siddique¹

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Artificial intelligence systems have become proficient at linking environmental features to targets to describe simple patterns in data. However, these systems can struggle with many real-world problems that entail hierarchical patterns within patterns, for example, in recognizing object ontologies where one object is made-up of other objects. Although it is possible to capture such complex structures by utilizing state-of-the-art deep networks, the knowledge is often stored in layers that do not take advantage of the potential benefits provided by reusing patterns within a layer of the system. Biological nervous systems can learn knowledge from simple and small-scale problems and then apply it to resolve more complex and large-scale problems in similar and related domains. However, rudimentary attempts to apply this transfer learning in artificial intelligence systems have struggled. This may be due to the homogeneous nature of their knowledge representation. The current understanding of the learning mechanisms in the brains of human and non-human animals can be used as inspiration to improve learning in artificial agents. Research into lateral asymmetry of the brain shows that it enables modular learning at different levels of abstraction that facilitate transfer between tasks. The proposed thesis is that an artificial intelligence system that enables lateralization and modular learning at different levels of abstraction has the ability to solve complex hierarchical problems that a similar homogeneous system can not. The comprehensive goal of this thesis is to accomplish lateralized learning, inspired by the principles of biological intelligence, in artificial intelligence systems. The objectives are to show that lateralization and modular learning assist the novel systems to encapsulate the underlying knowledge patterns in the form of building blocks of knowledge. These building blocks of knowledge are to be tested on analyzable Boolean tasks as well as practical computer vision and navigation tasks. Academic contributions are related to the novel methods of the linking, transfer, and sharing of learned knowledge which are based on the analogous strategies of the brain. This thesis proposes a general framework for lateralized artificial intelligence systems. The novel lateralized framework spans key aspects of knowledge perception, knowledge representation and utilization, and patterns of connectivity. It determines the essential functionality, critical methods, and associated parameters that are required to be incorporated into an artificial intelligence system to behave as a lateralized artificial intelligence system. This thesis creates a novel evolutionary machine learning system, by adapting the lateralized framework, to obtain a proof-of-concept of the lateralized approach. Considering the same problem at different levels of abstraction enables the novel system to reframe a complex problem as a simple problem and efficiently resolve it. The results on analyzable Boolean tasks show that the problems that contain a natural hierarchy of patterns are solved to a scale that exceeds previous work (i.e. 18-bit hierarchical multiplexer problem), and reusing learned general patterns as constituents for future problems advances transfer learning (e.g. n-bit parity problem effectively becomes a sequence of 2-bit parity problems). This thesis creates a novel lateralized artificial intelligence system, by adapting the lateralized framework, that shows robustness in a real-world domain that includes uncertainty, noise, and irrelevant and redundant data. The results of image classification tasks show that the lateralized system efficiently learns hierarchical distributions of knowledge, demonstrating performance that is similar to (or better than) other state-of-the-art deep systems as it reasons using multiple representations. Crucially, the novel system outperformed all the state-of-the-art deep models for the classification (binary classes) of normal and adversarial images by 0.43%-2.56% and 2.15%-25.84%, respectively. This thesis creates another novel multi-class lateralized system for computer vision problems to show that the lateralized approach can be scaled and not limited to learning classifier systems. Both the Boolean and computer vision problems are single step problems in the spatial domain. However, most biological tasks, which exhibit heterogeneity, are temporal in nature. This thesis creates a novel frame-of-reference based artificial intelligence system, by adapting the lateralized framework, to address perceptual aliasing in multi-step decision making tasks. Considering aliased states at a constituent level enables the novel system to place them appropriately in holistic level policies. Consequently, the novel system transforms a non-Markov environment into a deterministic environment and efficiently resolves it. Experimental results show that the novel system effectively solves complex aliasing patterns in non-Markov environments that have been challenging to artificial agents. For example, the novel system utilizes only 6.5, 3.71, and 3.22 steps to resolve Maze10, Littman57, and Woods102, respectively. A final contribution of this work is to obtain evidence of the benefits/costs of lateralization from artificial intelligence in order to inform cognitive neuroscience. Given that lateralization is ubiquitous in brains, evolutionary benefits can be assumed, at least in some domains. But that does not mean those benefits extend to all domains. The cognitive neuroscience research community has been struggling to determine the trade-off between the benefits and costs of lateralization. It has been hypothesized that lateralization has benefits that may counterbalance its costs. Lateralization has been associated with both poor and good performance. This thesis demonstrates the value of viable artificial systems for testing the costs and benefits of lateralization in biological systems.

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Solving Incremental Optimization Problems via Cooperative Coevolution

Cited by 15 publications

References 31 publications

Dynamic Multi-Objective Evolutionary Algorithms: An Overview

Dynamic Multi-Objective Evolutionary Algorithms: An Overview

Memory-Based Ant Colony System Approach for Multi-Source Data Associated Dynamic Electric Vehicle Dispatch Optimization

Lateralized Learning to Solve Complex Problems

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