A GA Based Combinatorial Auction Algorithm for Multi-Robot Cooperative Hunting

Gong, Jianya; Qi, Jianyong; Xiong, Guangming; Chen, Huiyan; Huang, Wanning

doi:10.1109/cis.2007.195

Cited by 17 publications

(13 citation statements)

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“…Researches on formation are also biologically-inspired [13,14]. As is often the case, researchers decompose the process of hunting behaviors into several steps [13,14], and plenty of approaches have been proposed to cope with the cases of more than one evader [15][16][17][18]. In [15], the authors threw light on the alliance conditions and designed one allied hunting strategy on account of the circular-elliptic besieging circles.…”

Section: Multirobot Cooperative Pursuitmentioning

confidence: 99%

Multiagent Robot Systems

Yang

2016

Advanced Technologies in Modern Robotic Applications

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In this chapter, we will first give an introduction of multiagent systems, which can serve as abstraction or simplified models for vast real-life complex systems, where local interactions among agents lead to complex global behaviors such as coordination, synchronization, formation, and so on. Then, as simple yet nontrivial examples of cooperation among robots, two typical cases of three-robot line formations are investigated and illustrated in a general mathematical framework of optimal multirobot formation. The robots moving with different speeds are expected to row on one straight line with the minimum formation time, so that the formation can be formulated in the most efficient way. Next, we investigate the hunting issue of a multirobot system in a dynamic environment. The proposed geometry-based strategy has the advantages of fast calculation and can be applied to three-dimensional space easily. At the end of the chapter, we investigate a few important problems in multirobot cooperative lifting control, and present an simulation study showing an example of four arms lifting one desk.

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Section: Multirobot Cooperative Pursuitmentioning

confidence: 99%

Multiagent Robot Systems

Yang

2016

Advanced Technologies in Modern Robotic Applications

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“…The solutions are chosen randomly from the entire solution space. More precisely, WD selects randomly a seller for each item, and then checks whether this selection is feasible or not by verifying two equations: the chosen seller has indeed bided for that item in (3), and the supply of seller is possible because he is still active for that time slot since he started transferring electricity to the buyer in (4). In case of an infeasible selection for an item, the algorithm tries another seller.…”

Section: Return the Top Ranked Solutionmentioning

confidence: 99%

“…Afterwards, based on their quality measurement i.e. their fitness value, the method improves the current population of solutions with three GA operators: selection (Gambling-Wheel Disk [4]), crossover (Modified Two-Point [14]) and mutation (Swap Mutation [13]. To prevent the population from having many similar solutions, the algorithm uses the diversity mechanism based on the enhanced crowding distance method given in [23].…”

Section: Return the Top Ranked Solutionmentioning

confidence: 99%

“…Designing an appropriate auction format in the electricity sector is a challenging task [4]; thus, in designing our particular CRA, we have attempted to determine the most relevant features and solving mechanisms that produce the best outcome in terms of solution quality and time-efficiency. Our auction consists of several items, each representing a time slot of 15 minutes.…”

Section: Contributionsmentioning

confidence: 99%

“…Furthermore, there has been a substantial increase in the number of online electricity auctions over the last decade due to their ability to improve allocative efficiency and to foster competition among power suppliers [1,3,4]. Auctions can be technology specific (one or more renewable energy) or neutral (past and new technology) [5].…”

Section: Scope and Problemmentioning

confidence: 99%

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Meeting peak electricity demand through combinatorial reverse auctioning of renewable energy

Shil

Sadaoui

2017

J. Mod. Power Syst. Clean Energy

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The option of organizing E-auctions to purchase electricity required for anticipated peak load period is a new one for utility companies. To meet the extra demand load, we develop electricity combinatorial reverse auction (CRA) for the purpose of procuring power from diverse energy sources. In this new, smart electricity market, suppliers of different scales can participate, and homeowners may even take an active role. In our CRA, an item, which is subject to several trading constraints, denotes a time slot that has two conflicting attributes, electricity quantity and price. To secure electricity, we design our auction with two bidding rounds: round one is exclusively for variable energy, and round two allows storage and nonintermittent renewable energy to bid on the remaining items. Our electricity auction leads to a complex winner determination (WD) task that we represent as a resource procurement optimization problem. We solve this problem using multi-objective genetic algorithms in order to find the trade-off solution that best lowers the price and increases the quantity. This solution consists of multiple winning suppliers, their prices, quantities and schedules. We validate our WD approach based on large-scale simulated datasets. We first assess the time-efficiency of our WD method, and we then compare it to well-known heuristic and exact WD techniques. In order to gain an exact idea about the accuracy of WD, we implement two famous exact algorithms for our constrained combinatorial procurement problem.

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