The Finder Novel

[Arlyne Doepner]

Jiais an avid reader who loves fantasy and young adult novels. She's also currently dipping her toes in the new adult genre but remains unconvinced by the prevalent need for traumatic pasts. Her favorite authors are Michelle West and Jacqueline Carey. YA authors whose works she's enjoyed include Holly Black, Laini Taylor, Ally Carter, and Megan Miranda. Jia's on a neverending quest for novels with diverse casts and multicultural settings. Feel free to email her with recommendations at [email protected]!

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He was studying Russian history, Soviet history, and Russian intelligence and military relations at the Harvard Russian Research Center. He thought he wanted to work in intelligence or teach Russian studies. He even had a job offer from the CIA.

As Finder grew older, his parents talked him out of becoming a novelist. Instead, he decided to work in intelligence and teach Russian studies. But first he had to study everything about Russia, which was how he ran into Hammer.

Morrison still made Finder revise it a couple of times. They went through three rounds with publishers trying to get a nibble anywhere, but nothing bubbled up. But then word got out the foreign rights had been sold and interest skyrocketed. Viking pounced, and Pam Dorman became his editor. Not long after, he had a hardcover bestseller. He was finally on his way. Years later, Publishers Weekly named The Moscow Club to its list of the ten best spy novels ever published. Go figure.

Like this? Read the chapters on Lee Child, Michael Connelly, Tess Gerritsen, Steve Berry, David Morrell, Gayle Lynds, Scott Turow, Lawrence Block, Randy Wayne White, Walter Mosley, Tom Straw. Michael Koryta, Harlan Coben, Jenny Milchman, James Grady, David Corbett. Robert Dugoni, David Baldacci, Steven James, Laura Lippman, Karen Dionne, Jon Land, S.A. Cosby, Diana Gabaldon, Tosca Lee, D.P. Lyle, James Patterson, Jeneva Rose, and Jeffery Deaver.

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Will Ferguson is the author of five novels, including 419, which won the Scotiabank Giller Prize. A three-time winner of the Stephen Leacock Medal for Humour, he has been nominated for both a Commonwealth Prize and an International IMPAC Dublin Literary Award. His most recent novels, The Finder and The Shoe on the Roof, were instant national bestsellers. Will Ferguson lives in Calgary. Visit him at WillFerguson.ca.

A Superior Court Judge who has a one-night stand. What else could possibly go wrong? For Judge Juliana Brody, everything. It turns out that her fling while visiting Chicago for a conference was not a random encounter.

Whenever I read a Joseph Finder novel, not only do I know that I will be entertained, there is always something new for me to learn which will help me grow as a thriller author. I only wish that I had access to the numerous contacts such as police officers, lawyers, judges, government operatives, and other professionals that he does who can help me with my research. Perhaps someday.

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Mobile robots are the robots that can move through the environment and be used in many applications, including the industrial environment, planet exploration, warehousing, and daily household chores. They can be controlled by an operator, set to do some specific jobs, or work autonomously. Robot path planning is the task of an autonomous robot to move safely from one position to another. In this paper, three new objective functions are introduced in the structure of improved grey wolf optimizer (IGWO) and improved particle swarm optimization (IPSO) for the robot path planning problems. As another part of our proposed method, a reduction of laser range finder (LRF) data is performed, and the avoidance collision approach is also introduced. Robots determine the next position by using LRF data and IGWO (IPSO) algorithms in a local approach. The initial and the goal positions are predefined for each robot. Moreover, the location of static obstacles and other robots are unknown for each robot. Finally, the experimental results of the robot path planning using IGWO are compared to different algorithms. The results indicate that the proposed method performs better in determining an optimal, short, safe, and smooth path. Also, it has less power and time consumption than other methods. All the algorithms are implemented in the V-REP robot simulator.

The role of path planning is acquiring a safe path by a robot in an environment from a predefined initial position to a target position with respect to optimal path and local constraints [36]. During the last decade, the field of path planning has been heavily studied in both academics and industry due to its applications in many real tasks like manufacturing, automobile, medical, industries, and so on. In the case of map representation, two discrete and continuous spaces could be considered based on environment information. The environment complexity changes under the influence of static and dynamic obstacles and even the presence of other robots on a common map. The strategy in which the robots achieve the goal based on environment information could be categorized into two local and global path planning problems [5]. In the global path planning [55], which is known as offline planning, the robots start moving after determining a collision-free trajectory from an initial position to a goal position. While in the local path planning or online planning [46], the robots navigate through the map step by step and find the next position toward the target.

Some various methods have been implemented on the motion or path planning problems. Approaches like roadmap, cell decomposition, potential field, and mathematical programming are classified in the classical approach. In the roadmap approach, the free C-space and the set of conceivable motions are reduced to the start and goal points in the C-Space that could be connected by a path. The two well-known roadmaps are visibility graph [1] and Voronoi diagram [4]. Šeda [45] compared the roadmap and cell decomposition approaches with each other. The potential field approach [3] uses the combination results of attraction and repulsion forces, and conducts the robot toward the target. The mathematical programming approach acts toward the trajectory planning problem as a numerical optimization problem such as MILP method [44]. Since the classical approach was incapable in some situations such as high dimensionality, time complexity, and trapping in local minima, the probabilistic approach has been developed. The probabilistic road maps (PRM) and rapidly-exploring random trees (RRT) are the most influential sampling-based motion planning algorithms which perform well in the high-dimensional state spaces [24].

Due to the above drawbacks of classical approaches, the other approaches like the heuristic approach have been developed in the field of artificial intelligence to fix the drawbacks. The heuristic and metaheuristic algorithms provide better performance because of their inherent features. Since the metaheuristic algorithms incorporate the local search and randomization method, they offer better equivalence among the diversification and intensification searches for the local and global optimums. The solution is detected much faster in heuristic algorithms compared to deterministic methods, but they do not guarantee to find it.

The metaheuristic algorithms are divided into two categories: individual solution algorithms and population based algorithms. The individual solution algorithms generate a random solution and improve the single solution until it reaches the optimum result. Tabu search (TS) and simulated annealing (SA) approaches are the well-known algorithms applied in different studies to solve the optimization problems. TS algorithm is used to find an optimal path in [29] for the first time. Nevertheless, the large number of iterations and too many parameters for adjustment can be considered as the drawbacks of TS algorithm. In [32], a SA approach is proposed to achieve an optimal or near optimal path for the mobile robot in the environments that include both static and dynamic obstacles. However, the algorithm suffers from slow convergence speed and long execution time, and its performance relies on the initial value.

On the other hand, in population based algorithms, a set of solutions are stochastically generated in a defined search space and the solutions will be updated in each iteration until the best value is achieved. The evolutionary and swarm intelligence are the metaheuristic algorithms which can solve the multi-objective optimization problems. Genetic algorithm (GA) is a famous optimization algorithm based on natural genetic that takes merits from the mechanisms such as natural selection, crossover, and mutation. It has the strong search capability and high search efficiency. In [37], the idea of using GA in robot path planning problem is introduced. Hocaoglu and Sanderson [21] proposed a novel iterative multi-resolution path planning method on the basis of GA. In some cases, the combination of GA and other algorithms provide better results. In [35], GA is used to optimize the trajectory with the assistance of the Neural Network approach, and it provides better learning in complex systems. However, it tends to premature convergence.

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