A Multi-world Intelligent Genetic Algorithm to Interactively Optimize Large-scale TSP

Sakurai, Yoshitaka; Onoyama, Takashi; Kubota, Sen; Nakamura, Yuna; Tsuruta, Setsuo

doi:10.1109/iri.2006.252421

Cited by 20 publications

(13 citation statements)

References 9 publications

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“…These methods are not familiar to field experts that they cannot optimize the delivery schedule interactively according to side conditions. In our former work, some types of Genetic Algorithms (GAs) such as a Multi-outer-world GA (Mow-GA) [20] and a Multi-inner-world Genetic Algorithm (Miw-GA) [21] were proposed. These GA incorporated simple heuristics such as NI type and 2-opt type heuristics aiming at interactive real-time response as well as avoiding significant errors for any kinds of delivery location patterns.…”

Section: B Genetic Algorithmsmentioning

confidence: 99%

A Refined Case Based Genetic Algorithm for Intelligent Route Optimization

Yamamoto

Kawabe

Kobayashi

et al. 2015

2015 11th International Conference on Signal-Image Technology &Amp; Internet-Based Systems (SITIS)

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This paper introduces a refined knowledge based approach to make Genetic Algorithms (GA) more effective. A concept called Case Based Reasoning (CBR) is combined with GA. This technology uses former solutions to the problem for deriving new solutions. The contribution of this CBR combination is a Case Base Maintenance technology that cares for both diversity and fitness in population. Former solutions namely, former problem's GA's solutions whose human / culture related problems are modified by human (expert) support, is maintained, selected, and used as GA's initial population for new but usually more than 80% similar problems. This technology weakens the factor "accidence" in favor of fitness and diversity balanced search especially towards human / culture related optimality. Instead of hoping for "accidentally good solutions" such technologies create "intentionally good solutions". Also, the Case Base (CB) combined with a new NI (Nearest Insertion) is used as a way to include / maintain human / cultural related knowledge into GA, which is called CBHOGA (Case Based Human Oriented GA). Human related knowledge is hard to formally express and mostly maintained by human support. This knowledge is kept in the Case Base and inherited as the initial population. Further, NI operations maintain this knowledge or preserve important parts of the solution in the initial population particularly by specifying insertion locations in the above mentioned new NI. The effects are shown by experiments.

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Section: B Genetic Algorithmsmentioning

confidence: 99%

A Refined Case Based Genetic Algorithm for Intelligent Route Optimization

Yamamoto

Kawabe

Kobayashi

et al. 2015

2015 11th International Conference on Signal-Image Technology &Amp; Internet-Based Systems (SITIS)

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“…They include Greedy Randomized Adaptive Search Procedure (GRASP) [5] and the elaborated random restart method [15], which apply local search such as 2-opt for improving initial solutions in a random fashion. Using such elaborated random restart methods, Mow-GA [19] was developed but could not guarantee an accuracy below 3% for TSPs of over 200 cities.…”

Section: Related Workmentioning

confidence: 99%

“…In [19] and [20], the experiment comparing among Mow-GA, Miw-GA, GA that used the best crossover in [3], Random-LK, GA by Van et al [22], and TS by Kanazawa, were conducted. However, none of them including the methods described above such as LKH can satisfy our real-time route scheduling optimization requirement (within 3 seconds, below 3% error in the worst case for TSPs with fewer than 2000 cities).…”

Section: Related Workmentioning

confidence: 99%

“…In our earlier work, some types of Genetic Algorithms (GAs) were proposed including a Multi-outer-world GA (Mow-GA) [19], a Multi-inner-world Genetic Algorithm (Miw-GA) [20], and a Backtrack/Restart GA (BR-GA) [21]. These GAs incorporated simple heuristics such as NI type heuristics aiming at interactive real-time responses as well as avoiding significant errors for any kinds of delivery location patterns.…”

Section: Introductionmentioning

confidence: 99%

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Case based human oriented delivery route optimization

Kawabe

Kobayashi

Tsuruta

et al. 2015

2015 IEEE Congress on Evolutionary Computation (CEC)

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Delivery route optimization is a well-known NPcomplete problem based on the Traveling Salesman Problem (TSP) involving 20-2000 cities though human oriented factors make the problem more complex. Despite of NP-completeness, the scheduling should be solved every time within interactive response time and below expert level error or local optimality, considering human oriented factors including personal, social, and cultural factors. To cope with this, Cases and NI (Nearest Insertion) are introduced into a Genetic Algorithm (GA), based on the insight that real problems are similar to previous ones. A solution can be derived from former solutions, considering human oriented factors as follows: (1) retrieving the most similar cases, (2) modifying them by removing and adding locations by NI, and (3) further optimizing them by a GA using only NI operations. This cannot only diminish the costs to compute new solutions from scratch but also inherit many parts of previous routes to respect human factors. Experimental evaluation revealed remarkable results. Though the most effective TSP solving method LKH needed more than 3 seconds, the proposed method yielded results within 3% of the worst error rate and in less than 3 seconds. Furthermore, the proposed method is able to inherit most of the delivery routes, while LKH leads to significant changes.

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“…world GA (Mow-GA) [10], which overcomes some 2-opt type GA's drawbacks by cascading NI type GA to 2-opt type GA.…”

Section: Introductionmentioning

confidence: 99%

A case based approach for an intelligent route optimization technology

Suzuki

Matsumaru

Tsuruta

et al. 2014

Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation

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The paper introduces a Case Based Approximation method to solve large scale Traveling Salesman Problems in a short time with a low error rate. It is useful for domains with most solutions being similar to solutions that have been created. Thus, a solution can be derived by (1) selecting a most similar TSP from a library of former TSP solutions, (2) removing the locations that are not part of the current TSP and (3) adding the missing locations of the current TSP by mutation, namely Nearest Insertion (NI). This way of creating solutions by Case Based Reasoning (CBR) avoids the computational costs to create new solutions from scratch.

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A Multi-world Intelligent Genetic Algorithm to Interactively Optimize Large-scale TSP

Cited by 20 publications

References 9 publications

A Refined Case Based Genetic Algorithm for Intelligent Route Optimization

A Refined Case Based Genetic Algorithm for Intelligent Route Optimization

Case based human oriented delivery route optimization

A case based approach for an intelligent route optimization technology

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